text:
@inproceedings{10.1145/1390334.1390338,
author = {Luo, Gang and Tang, Chunqiang},
title = {On Iterative Intelligent Medical Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390338},
doi = {10.1145/1390334.1390338},
abstract = {Searching for medical information on the Web has become highly popular, but it remains a challenging task because searchers are often uncertain about their exact medical situations and unfamiliar with medical terminology. To address this challenge, we have built an intelligent medical Web search engine called iMed, which uses medical knowledge and an interactive questionnaire to help searchers form queries. This paper focuses on iMed's iterative search advisor, which integrates medical and linguistic knowledge to help searchers improve search results iteratively. Such an iterative process is common for general Web search, and especially crucial for medical Web search, because searchers often miss desired search results due to their limited medical knowledge and the task's inherent difficulty. iMed's iterative search advisor helps the searcher in several ways. First, relevant symptoms and signs are automatically suggested based on the searcher's description of his situation. Second, instead of taking for granted the searcher's answers to the questions, iMed ranks and recommends alternative answers according to their likelihoods of being the correct answers. Third, related MeSH medical phrases are suggested to help the searcher refine his situation description. We demonstrate the effectiveness of iMed's iterative search advisor by evaluating it using real medical case records and USMLE medical exam questions.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {3–10},
numpages = {8},
keywords = {medical knowledge, medical query, iterative search process, language model, intelligent medical web search engine},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390339,
author = {Kumaran, Giridhar and Allan, James},
title = {Effective and Efficient User Interaction for Long Queries},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390339},
doi = {10.1145/1390334.1390339},
abstract = {Handling long queries can involve either pruning the query to retain only the important terms (reduction), or expanding the query to include related concepts (expansion). While automatic techniques to do so exist, roughly 25% performance improvements in terms of MAP have been realized in past work through interactive variants. We show that selectively reducing or expanding a query leads to an average improvement of 51% in MAP over the baseline for standard TREC test collections. We demonstrate how user interaction can be used to achieve this improvement. Most interaction techniques present users with a fixed number of options for all queries. We achieve improvements by interacting less with the user, i.e., we present techniques to identify the optimal number of options to present to users, resulting in an interface with an average of 70% fewer options to consider. Previous algorithms supporting interactive reduction and expansion are exponential in nature. To extend their utility to operational environments, we present techniques to make the complexity of the algorithms polynomial. We finally present an analysis of long queries that continue to exhibit poor performance in spite of our new techniques.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {11–18},
numpages = {8},
keywords = {query analysis, query expansion, user interaction, interactive retrieval efficiency, query reduction},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390340,
author = {Lin, Jimmy and Smucker, Mark D.},
title = {How Do Users Find Things with PubMed? Towards Automatic Utility Evaluation with User Simulations},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390340},
doi = {10.1145/1390334.1390340},
abstract = {In the context of document retrieval in the biomedical domain, this paper explores the complex relationship between the quality of initial query results and the overall utility of an interactive retrieval system. We demonstrate that a content-similarity browsing tool can compensate for poor retrieval results, and that the relationship between retrieval performance and overall utility is non-linear. Arguments are advanced with user simulations, which characterize the relevance of documents that a user might encounter with different browsing strategies. With broader implications to IR, this work provides a case study of how user simulations can be exploited as a formative tool for automatic utility evaluation. Simulation-based studies provide researchers with an additional evaluation tool to complement interactive and Cranfield-style experiments.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {19–26},
numpages = {8},
keywords = {find-similar, related article search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390342,
author = {Kalashnikov, Dmitri V. and Nuray-Turan, Rabia and Mehrotra, Sharad},
title = {Towards Breaking the Quality Curse.: A Web-Querying Approach to Web People Search.},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390342},
doi = {10.1145/1390334.1390342},
abstract = {Searching for people on the Web is one of the most common query types to the web search engines today. However, when a person name is queried, the returned webpages often contain documents related to several distinct namesakes who have the queried name. The task of disambiguating and finding the webpages related to the specific person of interest is left to the user. Many Web People Search (WePS) approaches have been developed recently that attempt to automate this disambiguation process. Nevertheless, the disambiguation quality of these techniques leaves a major room for improvement. This paper presents a new server-side WePS approach. It is based on collecting co-occurrence information from theWeb and thus it uses theWeb as an external data source. A skyline-based classification technique is developed for classifying the collected co-occurrence information in order to make clustering decisions. The clustering technique is specifically designed to (a) handle the dominance that exists in data and (b) to adapt to a given clustering quality measure. These properties allow the framework to get a major advantage in terms of result quality over all the latest WePS techniques we are aware of, including all the 18 methods covered in the recent WePS competition [2].},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {27–34},
numpages = {8},
keywords = {web querying, skyline based classifier, web people search, clustering, named entity co-occurrence, WEPS, social network},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390343,
author = {Wong, Tak-Lam and Lam, Wai and Wong, Tik-Shun},
title = {An Unsupervised Framework for Extracting and Normalizing Product Attributes from Multiple Web Sites},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390343},
doi = {10.1145/1390334.1390343},
abstract = {We have developed an unsupervised framework for simultaneously extracting and normalizing attributes of products from multiple Web pages originated from different sites. Our framework is designed based on a probabilistic graphical model that can model the page-independent content information and the page-dependent layout information of the text fragments in Web pages. One characteristic of our framework is that previously unseen attributes can be discovered from the clue contained in the layout format of the text fragments. Our framework tackles both extraction and normalization tasks by jointly considering the relationship between the content and layout information. Dirichlet process prior is employed leading to another advantage that the number of discovered product attributes is unlimited. An unsupervised inference algorithm based on variational method is presented. The semantics of the normalized attributes can be visualized by examining the term weights in the model. Our framework can be applied to a wide range of Web mining applications such as product matching and retrieval. We have conducted extensive experiments from four different domains consisting of over 300 Web pages from over 150 different Web sites, demonstrating the robustness and effectiveness of our framework.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {35–42},
numpages = {8},
keywords = {attribute normalization, web mining, attribute extraction},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390344,
author = {White, Ryen W. and Richardson, Matthew and Bilenko, Mikhail and Heath, Allison P.},
title = {Enhancing Web Search by Promoting Multiple Search Engine Use},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390344},
doi = {10.1145/1390334.1390344},
abstract = {Any given Web search engine may provide higher quality results than others for certain queries. Therefore, it is in users' best interest to utilize multiple search engines. In this paper, we propose and evaluate a framework that maximizes users' search effective-ness by directing them to the engine that yields the best results for the current query. In contrast to prior work on meta-search, we do not advocate for replacement of multiple engines with an aggregate one, but rather facilitate simultaneous use of individual engines. We describe a machine learning approach to supporting switching between search engines and demonstrate its viability at tolerable interruption levels. Our findings have implications for fluid competition between search engines.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {43–50},
numpages = {8},
keywords = {search engine switching},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390346,
author = {Webber, William and Moffat, Alistair and Zobel, Justin},
title = {Score Standardization for Inter-Collection Comparison of Retrieval Systems},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390346},
doi = {10.1145/1390334.1390346},
abstract = {The goal of system evaluation in information retrieval has always been to determine which of a set of systems is superior on a given collection. The tool used to determine system ordering is an evaluation metric such as average precision, which computes relative, collection-specific scores. We argue that a broader goal is achievable. In this paper we demonstrate that, by use of standardization, scores can be substantially independent of a particular collection, allowing systems to be compared even when they have been tested on different collections. Compared to current methods, our techniques provide richer information about system performance, improved clarity in outcome reporting, and greater simplicity in reviewing results from disparate sources.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {51–58},
numpages = {8},
keywords = {average precision, retrieval experiment, evaluation, system measurement},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390347,
author = {Al-Maskari, Azzah and Sanderson, Mark and Clough, Paul and Airio, Eija},
title = {The Good and the Bad System: Does the Test Collection Predict Users' Effectiveness?},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390347},
doi = {10.1145/1390334.1390347},
abstract = {Test collections are extensively used in the evaluation of information retrieval systems. Crucial to their use is the degree to which results from them predict user effectiveness. At first, past studies did not substantiate a relationship between system and user effectiveness; more recently, however, correlations have begun to emerge. The results of this paper strengthen and extend those findings. We introduce a novel methodology for investigating the relationship, which shows great success in establishing a significant correlation between system and user effectiveness. It is shown that users behave differently and discern differences between pairs of systems that have a very small absolute difference in test collection effectiveness. Our results strengthen the use of test collections in IR evaluation, confirming that users' effectiveness can be predicted successfully.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {59–66},
numpages = {8},
keywords = {user study, test collection, effectiveness measures},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390348,
author = {He, Ben and Macdonald, Craig and Ounis, Iadh},
title = {Retrieval Sensitivity under Training Using Different Measures},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390348},
doi = {10.1145/1390334.1390348},
abstract = {Various measures, such as binary preference (bpref), inferred average precision (infAP), and binary normalised discounted cumulative gain (nDCG) have been proposed as alternatives to mean average precision (MAP) for being less sensitive to the relevance judgements completeness. As the primary aim of any system building is to train the system to respond to user queries in a more robust and stable manner, in this paper, we investigate the importance of the choice of the evaluation measure for training, under different levels of evaluation incompleteness. We simulate evaluation incompleteness by sampling from the relevance assessments. Through large-scale experiments on two standard TREC test collections, we examine retrieval sensitivity when training - i.e. if a training process, based on any of the four discussed measures has an impact on the final retrieval performance. Experimental results show that training by bpref, infAP and nDCG provides significantly better retrieval performance than training by MAP when relevance judgements completeness is extremely low. When relevance judgements completeness increases, the measures behave more similarly.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {67–74},
numpages = {8},
keywords = {inferred average precision, relevance judgements, mean average precision, training, evaluation, optimisation, binary preference, normalised discounted cumulative gain},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390350,
author = {Mehta, Bhaskar and Nejdl, Wolfgang},
title = {Attack Resistant Collaborative Filtering},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390350},
doi = {10.1145/1390334.1390350},
abstract = {The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their benefit.While previous research has attempted to study the robustness of various existing Collaborative Filtering (CF) approaches, this remains an unsolved problem. Approaches such as Neighbor Selection algorithms, Association Rules and Robust Matrix Factorization have produced unsatisfactory results. This work describes a new collaborative algorithm based on SVD which is accurate as well as highly stable to shilling. This algorithm exploits previously established SVD based shilling detection algorithms, and combines it with SVD based-CF. Experimental results show a much diminished effect of all kinds of shilling attacks. This work also offers significant improvement over previous Robust Collaborative Filtering frameworks.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {75–82},
numpages = {8},
keywords = {collaborative filtering, shilling, recommendation algorithm, SVD},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390351,
author = {Liu, Nathan N. and Yang, Qiang},
title = {EigenRank: A Ranking-Oriented Approach to Collaborative Filtering},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390351},
doi = {10.1145/1390334.1390351},
abstract = {A recommender system must be able to suggest items that are likely to be preferred by the user. In most systems, the degree of preference is represented by a rating score. Given a database of users' past ratings on a set of items, traditional collaborative filtering algorithms are based on predicting the potential ratings that a user would assign to the unrated items so that they can be ranked by the predicted ratings to produce a list of recommended items. In this paper, we propose a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings. We measure the similarity between users based on the correlation between their rankings of the items rather than the rating values and propose new collaborative filtering algorithms for ranking items based on the preferences of similar users. Experimental results on real world movie rating data sets show that the proposed approach outperforms traditional collaborative filtering algorithms significantly on the NDCG measure for evaluating ranked results.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {83–90},
numpages = {8},
keywords = {ranking, collaborative filtering, random walk},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390352,
author = {Harpale, Abhay S. and Yang, Yiming},
title = {Personalized Active Learning for Collaborative Filtering},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390352},
doi = {10.1145/1390334.1390352},
abstract = {Collaborative Filtering (CF) requires user-rated training examples for statistical inference about the preferences of new users. Active learning strategies identify the most informative set of training examples through minimum interactions with the users. Current active learning approaches in CF make an implicit and unrealistic assumption that a user can provide rating for any queried item. This paper introduces a new approach to the problem which does not make such an assumption. We personalize active learning for the user, and query for only those items which the user can provide rating for. We propose an extended form of Bayesian active learning and use the Aspect Model for CF to illustrate and examine the idea. A comparative evaluation of the new method and a well-established baseline method on benchmark datasets shows statistically significant improvements with our method over the performance of the baseline method that is representative for existing approaches which do not take personalization into account.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {91–98},
numpages = {8},
keywords = {personalization, active learning, collaborative filtering, bayesian active learning, aspect model},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390354,
author = {Amini, Massih Reza and Truong, Tuong Vinh and Goutte, Cyril},
title = {A Boosting Algorithm for Learning Bipartite Ranking Functions with Partially Labeled Data},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390354},
doi = {10.1145/1390334.1390354},
abstract = {This paper presents a boosting based algorithm for learning a bipartite ranking function (BRF) with partially labeled data. Until now different attempts had been made to build a BRF in a transductive setting, in which the test points are given to the methods in advance as unlabeled data. The proposed approach is a semi-supervised inductive ranking algorithm which, as opposed to transductive algorithms, is able to infer an ordering on new examples that were not used for its training. We evaluate our approach using the TREC-9 Ohsumed and the Reuters-21578 data collections, comparing against two semi-supervised classification algorithms for ROCArea (AUC), uninterpolated average precision (AUP), mean precision@50 (TP) and Precision-Recall (PR) curves. In the most interesting cases where there are an unbalanced number of irrelevant examples over relevant ones, we show our method to produce statistically significant improvements with respect to these ranking measures.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {99–106},
numpages = {8},
keywords = {information routing, learning to rank with partially labeled data, boosting},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390355,
author = {Xu, Jun and Liu, Tie-Yan and Lu, Min and Li, Hang and Ma, Wei-Ying},
title = {Directly Optimizing Evaluation Measures in Learning to Rank},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390355},
doi = {10.1145/1390334.1390355},
abstract = {One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures used in information retrieval such as Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG). Several such algorithms including SVMmap and AdaRank have been proposed and their effectiveness has been verified. However, the relationships between the algorithms are not clear, and furthermore no comparisons have been conducted between them. In this paper, we conduct a study on the approach of directly optimizing evaluation measures in learning to rank for Information Retrieval (IR). We focus on the methods that minimize loss functions upper bounding the basic loss function defined on the IR measures. We first provide a general framework for the study and analyze the existing algorithms of SVMmap and AdaRank within the framework. The framework is based on upper bound analysis and two types of upper bounds are discussed. Moreover, we show that we can derive new algorithms on the basis of this analysis and create one example algorithm called PermuRank. We have also conducted comparisons between SVMmap, AdaRank, PermuRank, and conventional methods of Ranking SVM and RankBoost, using benchmark datasets. Experimental results show that the methods based on direct optimization of evaluation measures can always outperform conventional methods of Ranking SVM and RankBoost. However, no significant difference exists among the performances of the direct optimization methods themselves.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {107–114},
numpages = {8},
keywords = {learning to rank, evaluation measure, information retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390356,
author = {Geng, Xiubo and Liu, Tie-Yan and Qin, Tao and Arnold, Andrew and Li, Hang and Shum, Heung-Yeung},
title = {Query Dependent Ranking Using K-Nearest Neighbor},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390356},
doi = {10.1145/1390334.1390356},
abstract = {Many ranking models have been proposed in information retrieval, and recently machine learning techniques have also been applied to ranking model construction. Most of the existing methods do not take into consideration the fact that significant differences exist between queries, and only resort to a single function in ranking of documents. In this paper, we argue that it is necessary to employ different ranking models for different queries and onduct what we call query-dependent ranking. As the first such attempt, we propose a K-Nearest Neighbor (KNN) method for query-dependent ranking. We first consider an online method which creates a ranking model for a given query by using the labeled neighbors of the query in the query feature space and then rank the documents with respect to the query using the created model. Next, we give two offline approximations of the method, which create the ranking models in advance to enhance the efficiency of ranking. And we prove a theory which indicates that the approximations are accurate in terms of difference in loss of prediction, if the learning algorithm used is stable with respect to minor changes in training examples. Our experimental results show that the proposed online and offline methods both outperform the baseline method of using a single ranking function.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {115–122},
numpages = {8},
keywords = {k-nearest neighbor, query dependent ranking, stability},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390358,
author = {Dong, Wei and Charikar, Moses and Li, Kai},
title = {Asymmetric Distance Estimation with Sketches for Similarity Search in High-Dimensional Spaces},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390358},
doi = {10.1145/1390334.1390358},
abstract = {Efficient similarity search in high-dimensional spaces is important to content-based retrieval systems. Recent studies have shown that sketches can effectively approximate L1 distance in high-dimensional spaces, and that filtering with sketches can speed up similarity search by an order of magnitude. It is a challenge to further reduce the size of sketches, which are already compact, without compromising accuracy of distance estimation.This paper presents an efficient sketch algorithm for similarity search with L2 distances and a novel asymmetric distance estimation technique. Our new asymmetric estimator takes advantage of the original feature vector of the query to boost the distance estimation accuracy. We also apply this asymmetric method to existing sketches for cosine similarity and L1 distance. Evaluations with datasets extracted from images and telephone records show that our L2 sketch outperforms existing methods, and the asymmetric estimators consistently improve the accuracy of different sketch methods. To achieve the same search quality, asymmetric estimators can reduce the sketch size by 10% to 40%.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {123–130},
numpages = {8},
keywords = {sketch, similarity search, asymmetric distance estimation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390359,
author = {Skobeltsyn, Gleb and Junqueira, Flavio and Plachouras, Vassilis and Baeza-Yates, Ricardo},
title = {ResIn: A Combination of Results Caching and Index Pruning for High-Performance Web Search Engines},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390359},
doi = {10.1145/1390334.1390359},
abstract = {Results caching is an efficient technique for reducing the query processing load, hence it is commonly used in real search engines. This technique, however, bounds the maximum hit rate due to the large fraction of singleton queries, which is an important limitation. In this paper we propose ResIn - an architecture that uses a combination of results caching and index pruning to overcome this limitation.We argue that results caching is an inexpensive and efficient way to reduce the query processing load and show that it is cheaper to implement compared to a pruned index. At the same time, we show that index pruning performance is fundamentally affected by the changes in the query traffic that the results cache induces. We experiment with real query logs and a large document collection, and show that the combination of both techniques enables efficient reduction of the query processing costs and thus is practical to use in Web search engines.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {131–138},
numpages = {8},
keywords = {web search, caching, query logs, pruning},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390360,
author = {Brisaboa, Nieves R. and Fari\~{n}a, Antonio and Ladra, Susana and Navarro, Gonzalo},
title = {Reorganizing Compressed Text},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390360},
doi = {10.1145/1390334.1390360},
abstract = {Recent research has demonstrated beyond doubts the benefits of compressing natural language texts using word-based statistical semistatic compression. Not only it achieves extremely competitive compression rates, but also direct search on the compressed text can be carried out faster than on the original text; indexing based on inverted lists benefits from compression as well.Such compression methods assign a variable-length codeword to each different text word. Some coding methods (Plain Huffman and Restricted Prefix Byte Codes) do not clearly mark codeword boundaries, and hence cannot be accessed at random positions nor searched with the fastest text search algorithms. Other coding methods (Tagged Huffman, End-Tagged Dense Code, or (s, c)-Dense Code) do mark codeword boundaries, achieving a self-synchronization property that enables fast search and random access, in exchange for some loss in compression effectiveness.In this paper, we show that by just performing a simple reordering of the target symbols in the compressed text (more precisely, reorganizing the bytes into a wavelet-treelike shape) and using little additional space, searching capabilities are greatly improved without a drastic impact in compression and decompression times. With this approach, all the codes achieve synchronism and can be searched fast and accessed at arbitrary points. Moreover, the reordered compressed text becomes an implicitly indexed representation of the text, which can be searched for words in time independent of the text length. That is, we achieve not only fast sequential search time, but indexed search time, for almost no extra space cost.We experiment with three well-known word-based compression techniques with different characteristics (Plain Huffman, End-Tagged Dense Code and Restricted Prefix Byte Codes), and show the searching capabilities achieved by reordering the compressed representation on several corpora. We show that the reordered versions are not only much more efficient than their classical counterparts, but also more efficient than explicit inverted indexes built on the collection, when using the same amount of space.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {139–146},
numpages = {8},
keywords = {searching compressed text, compressed indexing, word-based compression},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390362,
author = {Smith, Catherine L. and Kantor, Paul B.},
title = {User Adaptation: Good Results from Poor Systems},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390362},
doi = {10.1145/1390334.1390362},
abstract = {Several recent studies have found only a weak relationship between the performance of a retrieval system and the "success" achievable by human searchers. We hypothesize that searchers are successful precisely because they alter their behavior. To explore the possible causal relation between system performance and search behavior, we control system performance, hoping to elicit adaptive search behaviors. 36 subjects each completed 12 searches using either a standard system or one of two degraded systems. Using a general linear model, we isolate the main effect of system performance, by measuring and removing main effects due to searcher variation, topic difficulty, and the position of each search in the time series. We find that searchers using our degraded systems are as successful as those using the standard system, but that, in achieving this success, they alter their behavior in ways that could be measured, in real time, by a suitably instrumented system. Our findings suggest, quite generally, that some aspects of behavioral dynamics may provide unobtrusive indicators of system performance.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {147–154},
numpages = {8},
keywords = {analysis techniques, adaptive IR systems, user modeling, experiment design},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390363,
author = {Xu, Shengliang and Bao, Shenghua and Fei, Ben and Su, Zhong and Yu, Yong},
title = {Exploring Folksonomy for Personalized Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390363},
doi = {10.1145/1390334.1390363},
abstract = {As a social service in Web 2.0, folksonomy provides the users the ability to save and organize their bookmarks online with "social annotations" or "tags". Social annotations are high quality descriptors of the web pages' topics as well as good indicators of web users' interests. We propose a personalized search framework to utilize folksonomy for personalized search. Specifically, three properties of folksonomy, namely the categorization, keyword, and structure property, are explored. In the framework, the rank of a web page is decided not only by the term matching between the query and the web page's content but also by the topic matching between the user's interests and the web page's topics. In the evaluation, we propose an automatic evaluation framework based on folksonomy data, which is able to help lighten the common high cost in personalized search evaluations. A series of experiments are conducted using two heterogeneous data sets, one crawled from Del.icio.us and the other from Dogear. Extensive experimental results show that our personalized search approach can significantly improve the search quality.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {155–162},
numpages = {8},
keywords = {folksonomy, personalized search, Web 2.0, automatic evaluation framework, topic space},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390364,
author = {Teevan, Jaime and Dumais, Susan T. and Liebling, Daniel J.},
title = {To Personalize or Not to Personalize: Modeling Queries with Variation in User Intent},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390364},
doi = {10.1145/1390334.1390364},
abstract = {In most previous work on personalized search algorithms, the results for all queries are personalized in the same manner. However, as we show in this paper, there is a lot of variation across queries in the benefits that can be achieved through personalization. For some queries, everyone who issues the query is looking for the same thing. For other queries, different people want very different results even though they express their need in the same way. We examine variability in user intent using both explicit relevance judgments and large-scale log analysis of user behavior patterns. While variation in user behavior is correlated with variation in explicit relevance judgments the same query, there are many other factors, such as result entropy, result quality, and task that can also affect the variation in behavior. We characterize queries using a variety of features of the query, the results returned for the query, and people's interaction history with the query. Using these features we build predictive models to identify queries that can benefit from personalization.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {163–170},
numpages = {8},
keywords = {clarity, personalized search, potential for personalization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390366,
author = {Kurland, Oren},
title = {The Opposite of Smoothing: A Language Model Approach to Ranking Query-Specific Document Clusters},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390366},
doi = {10.1145/1390334.1390366},
abstract = {Exploiting information induced from (query-specific) clustering of top-retrieved documents has long been proposed as means for improving precision at the very top ranks of the returned results. We present a novel language model approach to ranking query-specific clusters by the presumed percentage of relevant documents that they contain. While most previous cluster ranking approaches focus on the cluster as a whole, our model also exploits information induced from documents associated with the cluster. Our model substantially outperforms previous approaches for identifying clusters containing a high relevant-document percentage. Furthermore, using the model to produce document ranking yields precision-at-top-ranks performance that is consistently better than that of the initial ranking upon which clustering is performed; the performance also favorably compares with that of a state-of-the-art pseudo-feedback retrieval method.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {171–178},
numpages = {8},
keywords = {cluster-based language models, cluster-ranking, query-specific clusters, language models, ad hoc retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390367,
author = {Hu, Jian and Fang, Lujun and Cao, Yang and Zeng, Hua-Jun and Li, Hua and Yang, Qiang and Chen, Zheng},
title = {Enhancing Text Clustering by Leveraging Wikipedia Semantics},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390367},
doi = {10.1145/1390334.1390367},
abstract = {Most traditional text clustering methods are based on "bag of words" (BOW) representation based on frequency statistics in a set of documents. BOW, however, ignores the important information on the semantic relationships between key terms. To overcome this problem, several methods have been proposed to enrich text representation with external resource in the past, such as WordNet. However, many of these approaches suffer from some limitations: 1) WordNet has limited coverage and has a lack of effective word-sense disambiguation ability; 2) Most of the text representation enrichment strategies, which append or replace document terms with their hypernym and synonym, are overly simple. In this paper, to overcome these deficiencies, we first propose a way to build a concept thesaurus based on the semantic relations (synonym, hypernym, and associative relation) extracted from Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance traditional content similarity measure for text clustering. The experimental results on Reuters and OHSUMED datasets show that with the help of Wikipedia thesaurus, the clustering performance of our method is improved as compared to previous methods. In addition, with the optimized weights for hypernym, synonym, and associative concepts that are tuned with the help of a few labeled data users provided, the clustering performance can be further improved.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {179–186},
numpages = {8},
keywords = {similarity measure, text clustering, Wikipedia, thesaurus},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390368,
author = {Li, Tao and Ding, Chris and Zhang, Yi and Shao, Bo},
title = {Knowledge Transformation from Word Space to Document Space},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390368},
doi = {10.1145/1390334.1390368},
abstract = {In most IR clustering problems, we directly cluster the documents, working in the document space, using cosine similarity between documents as the similarity measure. In many real-world applications, however, we usually have knowledge on the word side and wish to transform this knowledge to the document (concept) side. In this paper, we provide a mechanism for this knowledge transformation. To the best of our knowledge, this is the first model for such type of knowledge transformation. This model uses a nonnegative matrix factorization model X = FSGT, where X is the word document semantic matrix, F is the posterior probability of a word belonging to a word cluster and represents knowledge in the word space, G is the posterior probability of a document belonging to a document cluster and represents knowledge in the document space, and S is a scaled matrix factor which provides a condensed view of X. We show how knowledge on words can improve document clustering, i.e, knowledge in the word space is transformed into the document space. We perform extensive experiments to validate our approach.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {187–194},
numpages = {8},
keywords = {knowledge transformation, clustering},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390370,
author = {Tsai, Ming-Feng and Wang, Yu-Ting and Chen, Hsin-Hsi},
title = {A Study of Learning a Merge Model for Multilingual Information Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390370},
doi = {10.1145/1390334.1390370},
abstract = {This paper proposes a learning approach for the merging process in multilingual information retrieval (MLIR). To conduct the learning approach, we also present a large number of features that may influence the MLIR merging process; these features are mainly extracted from three levels: query, document, and translation. After the feature extraction, we then use the FRank ranking algorithm to construct a merge model; to our knowledge, this practice is the first attempt to use a learning-based ranking algorithm to construct a merge model for MLIR merging. In our experiments, three test collections for the task of crosslingual information retrieval (CLIR) in NTCIR3, 4, and 5 are employed to assess the performance of our proposed method; moreover, several merging methods are also carried out for a comparison, including traditional merging methods, the 2-step merging strategy, and the merging method based on logistic regression. The experimental results show that our method can significantly improve merging quality on two different types of datasets. In addition to the effectiveness, through the merge model generated by FRank, our method can further identify key factors that influence the merging process; this information might provide us more insight and understanding into MLIR merging.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {195–202},
numpages = {8},
keywords = {MLIR, Merge Model, FRank},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390371,
author = {Wu, Yejun and Oard, Douglas W.},
title = {Bilingual Topic Aspect Classification with a Few Training Examples},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390371},
doi = {10.1145/1390334.1390371},
abstract = {This paper explores topic aspect (i.e., subtopic or facet) classification for English and Chinese collections. The evaluation model assumes a bilingual user who has found documents on a topic and identified a few passages in each language on aspects of that topic. Additional passages are then automatically labeled using a k-Nearest-Neighbor classifier and local (i.e., result set) Latent Semantic Analysis. Experiments show that when few training examples are available in either language, classification using training examples from both languages can often achieve higher effectiveness than using training examples from just one language. When the total number of training examples is held constant, classification effectiveness correlates positively with the fraction of same-language training examples in the training set. These results suggest that supervised classification can benefit from hand-annotating a few same-language examples, and that when performing classification in bilingual collections it is useful to label some examples in each language.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {203–210},
numpages = {8},
keywords = {cross-language, classification, test collection, subtopic},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390372,
author = {Joshi, Tanuja and Joy, Joseph and Kellner, Tobias and Khurana, Udayan and Kumaran, A and Sengar, Vibhuti},
title = {Crosslingual Location Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390372},
doi = {10.1145/1390334.1390372},
abstract = {Address geocoding, the process of finding the map location for a structured postal address, is a relatively well-studied problem. In this paper we consider the more general problem of crosslingual location search, where the queries are not limited to postal addresses, and the language and script used in the search query is different from the one in which the underlying data is stored. To the best of our knowledge, our system is the first crosslingual location search system that is able to geocode complex addresses. We use a statistical machine transliteration system to convert location names from the script of the query to that of the stored data. However, we show that it is not sufficient to simply feed the resulting transliterations into a monolingual geocoding system, as the ambiguity inherent in the conversion drastically expands the location search space and significantly lowers the quality of results. The strength of our approach lies in its integrated, end-to-end nature: we use abstraction and fuzzy search (in the text domain) to achieve maximum coverage despite transliteration ambiguities, while applying spatial constraints (in the geographic domain) to focus only on viable interpretations of the query. Our experiments with structured and unstructured queries in a set of diverse languages and scripts (Arabic, English, Hindi and Japanese) searching for locations in different regions of the world, show full crosslingual location search accuracy at levels comparable to that of commercial monolingual systems. We achieve these levels of performance using techniques that may be applied to crosslingual searches in any language/script, and over arbitrary spatial data.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {211–218},
numpages = {8},
keywords = {address geocoding, crosslingual information retrieval, location search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390374,
author = {Wang, Xuanhui and Fang, Hui and Zhai, ChengXiang},
title = {A Study of Methods for Negative Relevance Feedback},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390374},
doi = {10.1145/1390334.1390374},
abstract = {Negative relevance feedback is a special case of relevance feedback where we do not have any positive example; this often happens when the topic is difficult and the search results are poor. Although in principle any standard relevance feedback technique can be applied to negative relevance feedback, it may not perform well due to the lack of positive examples. In this paper, we conduct a systematic study of methods for negative relevance feedback. We compare a set of representative negative feedback methods, covering vector-space models and language models, as well as several special heuristics for negative feedback. Evaluating negative feedback methods requires a test set with sufficient difficult topics, but there are not many naturally difficult topics in the existing test collections. We use two sampling strategies to adapt a test collection with easy topics to evaluate negative feedback. Experiment results on several TREC collections show that language model based negative feedback methods are generally more effective than those based on vector-space models, and using multiple negative models is an effective heuristic for negative feedback. Our results also show that it is feasible to adapt test collections with easy topics for evaluating negative feedback methods through sampling.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {219–226},
numpages = {8},
keywords = {difficult topics, language models, vector space models, negative feedback},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390375,
author = {Xu, Zuobing and Akella, Ram},
title = {A Bayesian Logistic Regression Model for Active Relevance Feedback},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390375},
doi = {10.1145/1390334.1390375},
abstract = {Relevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performance. The traditional relevance feedback algorithms lead to overfitting because of the limited amount of training data and large term space. This paper introduces an online Bayesian logistic regression algorithm to incorporate relevance feedback information. The new approach addresses the overfitting problem by projecting the original feature space onto a more compact set which retains the necessary information. The new set of features consist of the original retrieval score, the distance to the relevant documents and the distance to non-relevant documents. To reduce the human evaluation effort in ascertaining relevance, we introduce a new active learning algorithm based on variance reduction to actively select documents for user evaluation. The new active learning algorithm aims to select feedback documents to reduce the model variance. The variance reduction approach leads to capturing relevance, diversity and uncertainty of the unlabeled documents in a principled manner. These are the critical factors of active learning indicated in previous literature. Experiments with several TREC datasets demonstrate the effectiveness of the proposed approach.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {227–234},
numpages = {8},
keywords = {active learning, bayesian logistic regression, relevance feedback},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390376,
author = {Lee, Kyung Soon and Croft, W. Bruce and Allan, James},
title = {A Cluster-Based Resampling Method for Pseudo-Relevance Feedback},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390376},
doi = {10.1145/1390334.1390376},
abstract = {Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select better pseudo-relevant documents based on the relevance model. The main idea is to use document clusters to find dominant documents for the initial retrieval set, and to repeatedly feed the documents to emphasize the core topics of a query. Experimental results on large-scale web TREC collections show significant improvements over the relevance model. For justification of the resampling approach, we examine relevance density of feedback documents. A higher relevance density will result in greater retrieval accuracy, ultimately approaching true relevance feedback. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback. This result indicates that the proposed method is effective for pseudo-relevance feedback.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {235–242},
numpages = {8},
keywords = {dominant documents, query expansion, information retrieval, a cluster-based resampling, pseudo-relevance feedback},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390377,
author = {Cao, Guihong and Nie, Jian-Yun and Gao, Jianfeng and Robertson, Stephen},
title = {Selecting Good Expansion Terms for Pseudo-Relevance Feedback},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390377},
doi = {10.1145/1390334.1390377},
abstract = {Pseudo-relevance feedback assumes that most frequent terms in the pseudo-feedback documents are useful for the retrieval. In this study, we re-examine this assumption and show that it does not hold in reality - many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval. We also show that good expansion terms cannot be distinguished from bad ones merely on their distributions in the feedback documents and in the whole collection. We then propose to integrate a term classification process to predict the usefulness of expansion terms. Multiple additional features can be integrated in this process. Our experiments on three TREC collections show that retrieval effectiveness can be much improved when term classification is used. In addition, we also demonstrate that good terms should be identified directly according to their possible impact on the retrieval effectiveness, i.e. using supervised learning, instead of unsupervised learning.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {243–250},
numpages = {8},
keywords = {SVM, expansion term classification, language models, Pseudo-relevance feedback},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390379,
author = {Duh, Kevin and Kirchhoff, Katrin},
title = {Learning to Rank with Partially-Labeled Data},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390379},
doi = {10.1145/1390334.1390379},
abstract = {Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorithms has focused on cases where only labeled data is available for training (i.e. supervised learning). In this paper, we consider the question whether unlabeled (test) data can be exploited to improve ranking performance. We present a framework for transductive learning of ranking functions and show that the answer is affirmative. Our framework is based on generating better features from the test data (via KernelPCA) and incorporating such features via Boosting, thus learning different ranking functions adapted to the individual test queries. We evaluate this method on the LETOR (TREC, OHSUMED) dataset and demonstrate significant improvements.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {251–258},
numpages = {8},
keywords = {transductive learning, information retrieval, kernel principal components analysis, learning to rank, boosting},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390380,
author = {Guiver, John and Snelson, Edward},
title = {Learning to Rank with SoftRank and Gaussian Processes},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390380},
doi = {10.1145/1390334.1390380},
abstract = {In this paper we address the issue of learning to rank for document retrieval using Thurstonian models based on sparse Gaussian processes. Thurstonian models represent each document for a given query as a probability distribution in a score space; these distributions over scores naturally give rise to distributions over document rankings. However, in general we do not have observed rankings with which to train the model; instead, each document in the training set is judged to have a particular relevance level: for example "Bad", "Fair", "Good", or "Excellent". The performance of the model is then evaluated using information retrieval (IR) metrics such as Normalised Discounted Cumulative Gain (NDCG). Recently Taylor et al. presented a method called SoftRank which allows the direct gradient optimisation of a smoothed version of NDCG using a Thurstonian model. In this approach, document scores are represented by the outputs of a neural network, and score distributions are created artificially by adding random noise to the scores. The SoftRank mechanism is a general one; it can be applied to different IR metrics, and make use of different underlying models. In this paper we extend the SoftRank framework to make use of the score uncertainties which are naturally provided by a Gaussian process (GP), which is a probabilistic non-linear regression model. We further develop the model by using sparse Gaussian process techniques, which give improved performance and efficiency, and show competitive results against baseline methods when tested on the publicly available LETOR OHSUMED data set. We also explore how the available uncertainty information can be used in prediction and how it affects model performance.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {259–266},
numpages = {8},
keywords = {learning, ranking, information retrieval, Gaussian process},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390381,
author = {Veloso, Adriano A. and Almeida, Humberto M. and Gon\c{c}alves, Marcos A. and Meira Jr., Wagner},
title = {Learning to Rank at Query-Time Using Association Rules},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390381},
doi = {10.1145/1390334.1390381},
abstract = {Some applications have to present their results in the form of ranked lists. This is the case of many information retrieval applications, in which documents must be sorted according to their relevance to a given query. This has led the interest of the information retrieval community in methods that automatically learn effective ranking functions. In this paper we propose a novel method which uncovers patterns (or rules) in the training data associating features of the document with its relevance to the query, and then uses the discovered rules to rank documents. To address typical problems that are inherent to the utilization of association rules (such as missing rules and rule explosion), the proposed method generates rules on a demand-driven basis, at query-time. The result is an extremely fast and effective ranking method. We conducted a systematic evaluation of the proposed method using the LETOR benchmark collections. We show that generating rules on a demand-driven basis can boost ranking performance, providing gains ranging from 12% to 123%, outperforming the state-of-the-art methods that learn to rank, with no need of time-consuming and laborious pre-processing. As a highlight, we also show that additional information, such as query terms, can make the generated rules more discriminative, further improving ranking performance.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {267–274},
numpages = {8},
keywords = {association rules, ranking},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390382,
author = {Zhou, Ke and Xue, Gui-Rong and Zha, Hongyuan and Yu, Yong},
title = {Learning to Rank with Ties},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390382},
doi = {10.1145/1390334.1390382},
abstract = {Designing effective ranking functions is a core problem for information retrieval and Web search since the ranking functions directly impact the relevance of the search results. The problem has been the focus of much of the research at the intersection of Web search and machine learning, and learning ranking functions from preference data in particular has recently attracted much interest. The objective of this paper is to empirically examine several objective functions that can be used for learning ranking functions from preference data. Specifically, we investigate the roles of ties in the learning process. By ties, we mean preference judgments that two documents have equal degree of relevance with respect to a query. This type of data has largely been ignored or not properly modeled in the past. In this paper, we analyze the properties of ties and develop novel learning frameworks which combine ties and preference data using statistical paired comparison models to improve the performance of learned ranking functions. The resulting optimization problems explicitly incorporating ties and preference data are solved using gradient boosting methods. Experimental studies are conducted using three publicly available data sets which demonstrate the effectiveness of the proposed new methods.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {275–282},
numpages = {8},
keywords = {preference learning, gradient boosting, ties, paired comparison, functional gradient descent, ranking function, machine learning},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390384,
author = {Wei, Furu and Li, Wenjie and Lu, Qin and He, Yanxiang},
title = {Query-Sensitive Mutual Reinforcement Chain and Its Application in Query-Oriented Multi-Document Summarization},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390384},
doi = {10.1145/1390334.1390384},
abstract = {Sentence ranking is the issue of most concern in document summarization. Early researchers have presented the mutual reinforcement principle (MR) between sentence and term for simultaneous key phrase and salient sentence extraction in generic single-document summarization. In this work, we extend the MR to the mutual reinforcement chain (MRC) of three different text granularities, i.e., document, sentence and terms. The aim is to provide a general reinforcement framework and a formal mathematical modeling for the MRC. Going one step further, we incorporate the query influence into the MRC to cope with the need for query-oriented multi-document summarization. While the previous summarization approaches often calculate the similarity regardless of the query, we develop a query-sensitive similarity to measure the affinity between the pair of texts. When evaluated on the DUC 2005 dataset, the experimental results suggest that the proposed query-sensitive MRC (Qs-MRC) is a promising approach for summarization.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {283–290},
numpages = {8},
keywords = {mutual reinforcement chain, query-sensitive similarity, ranking algorithms, query-oriented summarization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390385,
author = {Hu, Meishan and Sun, Aixin and Lim, Ee-Peng},
title = {Comments-Oriented Document Summarization: Understanding Documents with Readers' Feedback},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390385},
doi = {10.1145/1390334.1390385},
abstract = {Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web document (e.g., a blog post) by considering not only its content, but also the comments left by its readers. We identify three relations (namely, topic, quotation, and mention) by which comments can be linked to one another, and model the relations in three graphs. The importance of each comment is then scored by: (i) graph-based method, where the three graphs are merged into a multi-relation graph; (ii) tensor-based method, where the three graphs are used to construct a 3rd-order tensor. To generate a comments-oriented summary, we extract sentences from the given Web document using either feature-biased approach or uniform-document approach. The former scores sentences to bias keywords derived from comments; while the latter scores sentences uniformly with comments. In our experiments using a set of blog posts with manually labeled sentences, our proposed summarization methods utilizing comments showed significant improvement over those not using comments. The methods using feature-biased sentence extraction approach were observed to outperform that using uniform-document approach.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {291–298},
numpages = {8},
keywords = {comments, document summarization, blog, graph-based scoring, tensor-based scoring},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390386,
author = {Wan, Xiaojun and Yang, Jianwu},
title = {Multi-Document Summarization Using Cluster-Based Link Analysis},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390386},
doi = {10.1145/1390334.1390386},
abstract = {The Markov Random Walk model has been recently exploited for multi-document summarization by making use of the link relationships between sentences in the document set, under the assumption that all the sentences are indistinguishable from each other. However, a given document set usually covers a few topic themes with each theme represented by a cluster of sentences. The topic themes are usually not equally important and the sentences in an important theme cluster are deemed more salient than the sentences in a trivial theme cluster. This paper proposes the Cluster-based Conditional Markov Random Walk Model (ClusterCMRW) and the Cluster-based HITS Model (ClusterHITS) to fully leverage the cluster-level information. Experimental results on the DUC2001 and DUC2002 datasets demonstrate the good effectiveness of our proposed summarization models. The results also demonstrate that the ClusterCMRW model is more robust than the ClusterHITS model, with respect to different cluster numbers.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {299–306},
numpages = {8},
keywords = {multi-document summarization, cluster-based link analysis, conditional Markov random walk model, HITS},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390387,
author = {Wang, Dingding and Li, Tao and Zhu, Shenghuo and Ding, Chris},
title = {Multi-Document Summarization via Sentence-Level Semantic Analysis and Symmetric Matrix Factorization},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390387},
doi = {10.1145/1390334.1390387},
abstract = {Multi-document summarization aims to create a compressed summary while retaining the main characteristics of the original set of documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence-level semantic analysis and symmetric non-negative matrix factorization. We first calculate sentence-sentence similarities using semantic analysis and construct the similarity matrix. Then symmetric matrix factorization, which has been shown to be equivalent to normalized spectral clustering, is used to group sentences into clusters. Finally, the most informative sentences are selected from each group to form the summary. Experimental results on DUC2005 and DUC2006 data sets demonstrate the improvement of our proposed framework over the implemented existing summarization systems. A further study on the factors that benefit the high performance is also conducted.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {307–314},
numpages = {8},
keywords = {symmetric non-negative matrix factorization, sentence-level semantic analysis, multi-document summarization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390389,
author = {Pickens, Jeremy and Golovchinsky, Gene and Shah, Chirag and Qvarfordt, Pernilla and Back, Maribeth},
title = {Algorithmic Mediation for Collaborative Exploratory Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390389},
doi = {10.1145/1390334.1390389},
abstract = {We describe a new approach to information retrieval: algorithmic mediation for intentional, synchronous collaborative exploratory search. Using our system, two or more users with a common information need search together, simultaneously. The collaborative system provides tools, user interfaces and, most importantly, algorithmically-mediated retrieval to focus, enhance and augment the team's search and communication activities. Collaborative search outperformed post hoc merging of similarly instrumented single user runs. Algorithmic mediation improved both collaborative search (allowing a team of searchers to find relevant information more efficiently and effectively), and exploratory search (allowing the searchers to find relevant information that cannot be found while working individually).},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {315–322},
numpages = {8},
keywords = {interactive retrieval, algorithmic mediation, collaborative search, evaluation, retrieval models},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390390,
author = {Zimmer, Christian and Tryfonopoulos, Christos and Weikum, Gerhard},
title = {Exploiting Correlated Keywords to Improve Approximate Information Filtering},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390390},
doi = {10.1145/1390334.1390390},
abstract = {Information filtering, also referred to as publish/subscribe, complements one-time searching since users are able to subscribe to information sources and be notified whenever new documents of interest are published. In approximate information filtering only selected information sources, that are likely to publish documents relevant to the user interests in the future, are monitored. To achieve this functionality, a subscriber exploits statistical metadata to identify promising publishers and index its continuous query only in those publishers. The statistics are maintained in a directory, usually on a per-keyword basis, thus disregarding possible correlations among keywords. Using this coarse information, poor publisher selection may lead to poor filtering performance and thus loss of interesting documents.1Based on the above observation, this work extends query routing techniques from the domain of distributed information retrieval in peer-to-peer (P2P) networks, and provides new algorithms for exploiting the correlation among keywords in a filtering setting. We develop and evaluate two algorithms based on single-key and multi-key statistics and utilize two different synopses (Hash Sketches and KMV synopses) to compactly represent publishers. Our experimental evaluation using two real-life corpora with web and blog data demonstrates the filtering effectiveness of both approaches and highlights the different tradeoffs.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {323–330},
numpages = {8},
keywords = {distributed information filtering (IF), distinct-value (DV) estimation, Peer-to-Peer (P2P), information systems, approximate publish/subscribe},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390392,
author = {Dupret, Georges E. and Piwowarski, Benjamin},
title = {A User Browsing Model to Predict Search Engine Click Data from Past Observations.},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390392},
doi = {10.1145/1390334.1390392},
abstract = {Search engine click logs provide an invaluable source of relevance information but this information is biased because we ignore which documents from the result list the users have actually seen before and after they clicked. Otherwise, we could estimate document relevance by simple counting. In this paper, we propose a set of assumptions on user browsing behavior that allows the estimation of the probability that a document is seen, thereby providing an unbiased estimate of document relevance. To train, test and compare our model to the best alternatives described in the Literature, we gather a large set of real data and proceed to an extensive cross-validation experiment. Our solution outperforms very significantly all previous models. As a side effect, we gain insight into the browsing behavior of users and we can compare it to the conclusions of an eye-tracking experiments by Joachims et al. [12]. In particular, our findings confirm that a user almost always see the document directly after a clicked document. They also explain why documents situated just after a very relevant document are clicked more often.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {331–338},
numpages = {8},
keywords = {user behavior, search engines, statistical model, clickthrough data},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390393,
author = {Li, Xiao and Wang, Ye-Yi and Acero, Alex},
title = {Learning Query Intent from Regularized Click Graphs},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390393},
doi = {10.1145/1390334.1390393},
abstract = {This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a unified user interface. Previous works on query classification have primarily focused on improving feature representation of queries, e.g., by augmenting queries with search engine results. In this work, we investigate a completely orthogonal approach --- instead of enriching feature representation, we aim at drastically increasing the amounts of training data by semi-supervised learning with click graphs. Specifically, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph. Moreover, we regularize the learning with click graphs by content-based classification to avoid propagating erroneous labels. We demonstrate the effectiveness of our algorithms in two different applications, product intent and job intent classification. In both cases, we expand the training data with automatically labeled queries by over two orders of magnitude, leading to significant improvements in classification performance. An additional finding is that with a large amount of training data obtained in this fashion, classifiers using only query words/phrases as features can work remarkably well.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {339–346},
numpages = {8},
keywords = {click-through data, semi-supervised learning, query classification, user intent},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390394,
author = {Elsas, Jonathan L. and Arguello, Jaime and Callan, Jamie and Carbonell, Jaime G.},
title = {Retrieval and Feedback Models for Blog Feed Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390394},
doi = {10.1145/1390334.1390394},
abstract = {Blog feed search poses different and interesting challenges from traditional ad hoc document retrieval. The units of retrieval, the blogs, are collections of documents, the blog posts. In this work we adapt a state-of-the-art federated search model to the feed retrieval task, showing a significant improvement over algorithms based on the best performing submissions in the TREC 2007 Blog Distillation task[12]. We also show that typical query expansion techniques such as pseudo-relevance feedback using the blog corpus do not provide any significant performance improvement and in many cases dramatically hurt performance. We perform an in-depth analysis of the behavior of pseudo-relevance feedback for this task and develop a novel query expansion technique using the link structure in Wikipedia. This query expansion technique provides significant and consistent performance improvements for this task, yielding a 22% and 14% improvement in MAP over the unexpanded query for our baseline and federated algorithms respectively.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {347–354},
numpages = {8},
keywords = {query expansion, federated search, blog retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390396,
author = {Wang, Changhu and Zhang, Lei and Zhang, Hong-Jiang},
title = {Learning to Reduce the Semantic Gap in Web Image Retrieval and Annotation},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390396},
doi = {10.1145/1390334.1390396},
abstract = {We study in this paper the problem of bridging the semantic gap between low-level image features and high-level semantic concepts, which is the key hindrance in content-based image retrieval. Piloted by the rich textual information of Web images, the proposed framework tries to learn a new distance measure in the visual space, which can be used to retrieve more semantically relevant images for any unseen query image. The framework differentiates with traditional distance metric learning methods in the following ways. 1) A ranking-based distance metric learning method is proposed for image retrieval problem, by optimizing the leave-one-out retrieval performance on the training data. 2) To be scalable, millions of images together with rich textual information have been crawled from the Web to learn the similarity measure, and the learning framework particularly considers the indexing problem to ensure the retrieval efficiency. 3) To alleviate the noises in the unbalanced labels of images and fully utilize the textual information, a Latent Dirichlet Allocation based topic-level text model is introduced to define pairwise semantic similarity between any two images. The learnt distance measure can be directly applied to applications such as content-based image retrieval and search-based image annotation. Experimental results on the two applications in a two million Web image database show both the effectiveness and efficiency of the proposed framework.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {355–362},
numpages = {8},
keywords = {ranking-based distance metric learning, content-based image retrieval, search-based image annotation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390397,
author = {Chia, Tee Kiah and Sim, Khe Chai and Li, Haizhou and Ng, Hwee Tou},
title = {A Lattice-Based Approach to Query-by-Example Spoken Document Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390397},
doi = {10.1145/1390334.1390397},
abstract = {Recent efforts on the task of spoken document retrieval (SDR) have made use of speech lattices: speech lattices contain information about alternative speech transcription hypotheses other than the 1-best transcripts, and this information can improve retrieval accuracy by overcoming recognition errors present in the 1-best transcription. In this paper, we look at using lattices for the query-by-example spoken document retrieval task - retrieving documents from a speech corpus, where the queries are themselves in the form of complete spoken documents (query exemplars). We extend a previously proposed method for SDR with short queries to the query-by-example task. Specifically, we use a retrieval method based on statistical modeling: we compute expected word counts from document and query lattices, estimate statistical models from these counts, and compute relevance scores as divergences between these models. Experimental results on a speech corpus of conversational English show that the use of statistics from lattices for both documents and query exemplars results in better retrieval accuracy than using only 1-best transcripts for either documents, or queries, or both. In addition, we investigate the effect of stop word removal which further improves retrieval accuracy. To our knowledge, our work is the first to have used a lattice-based approach to query-by-example spoken document retrieval.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {363–370},
numpages = {8},
keywords = {query by example, retrieval of conversational telephone speech, lattice-based spoken document retrieval, language modeling, probabilistic retrieval approach},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390399,
author = {Balog, Krisztian and Weerkamp, Wouter and de Rijke, Maarten},
title = {A Few Examples Go a Long Way: Constructing Query Models from Elaborate Query Formulations},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390399},
doi = {10.1145/1390334.1390399},
abstract = {We address a specific enterprise document search scenario, where the information need is expressed in an elaborate manner. In our scenario, information needs are expressed using a short query (of a few keywords) together with examples of key reference pages. Given this setup, we investigate how the examples can be utilized to improve the end-to-end performance on the document retrieval task. Our approach is based on a language modeling framework, where the query model is modified to resemble the example pages. We compare several methods for sampling expansion terms from the example pages to support query-dependent and query-independent query expansion; the latter is motivated by the wish to increase "aspect recall", and attempts to uncover aspects of the information need not captured by the query.For evaluation purposes we use the CSIRO data set created for the TREC 2007 Enterprise track. The best performance is achieved by query models based on query-independent sampling of expansion terms from the example documents.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {371–378},
numpages = {8},
keywords = {query modeling, language models, query expansion, enterprise search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390400,
author = {Guo, Jiafeng and Xu, Gu and Li, Hang and Cheng, Xueqi},
title = {A Unified and Discriminative Model for Query Refinement},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390400},
doi = {10.1145/1390334.1390400},
abstract = {This paper addresses the issue of query refinement, which involves reformulating ill-formed search queries in order to enhance relevance of search results. Query refinement typically includes a number of tasks such as spelling error correction, word splitting, word merging, phrase segmentation, word stemming, and acronym expansion. In previous research, such tasks were addressed separately or through employing generative models. This paper proposes employing a unified and discriminative model for query refinement. Specifically, it proposes a Conditional Random Field (CRF) model suitable for the problem, referred to as Conditional Random Field for Query Refinement (CRF-QR). Given a sequence of query words, CRF-QR predicts a sequence of refined query words as well as corresponding refinement operations. In that sense, CRF-QR differs greatly from conventional CRF models. Two types of CRF-QR models, namely a basic model and an extended model are introduced. One merit of employing CRF-QR is that different refinement tasks can be performed simultaneously and thus the accuracy of refinement can be enhanced. Furthermore, the advantages of discriminative models over generative models can be fully leveraged. Experimental results demonstrate that CRF-QR can significantly outperform baseline methods. Furthermore, when CRF-QR is used in web search, a significant improvement of relevance can be obtained.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {379–386},
numpages = {8},
keywords = {web search, conditional random fields, query refinement},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390401,
author = {Buscher, Georg and Dengel, Andreas and van Elst, Ludger},
title = {Query Expansion Using Gaze-Based Feedback on the Subdocument Level},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390401},
doi = {10.1145/1390334.1390401},
abstract = {We examine the effect of incorporating gaze-based attention feedback from the user on personalizing the search process. Employing eye tracking data, we keep track of document parts the user read in some way. We use this information on the subdocument level as implicit feedback for query expansion and reranking.We evaluated three different variants incorporating gaze data on the subdocument level and compared them against a baseline based on context on the document level. Our results show that considering reading behavior as feedback yields powerful improvements of the search result accuracy of ca. 32% in the general case. However, the extent of the improvements varies depending on the internal structure of the viewed documents and the type of the current information need.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {387–394},
numpages = {8},
keywords = {eye tracking, implicit feedback, reading, personalization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390403,
author = {Arapakis, Ioannis and Jose, Joemon M. and Gray, Philip D.},
title = {Affective Feedback: An Investigation into the Role of Emotions in the Information Seeking Process},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390403},
doi = {10.1145/1390334.1390403},
abstract = {User feedback is considered to be a critical element in the information seeking process, especially in relation to relevance assessment. Current feedback techniques determine content relevance with respect to the cognitive and situational levels of interaction that occurs between the user and the retrieval system. However, apart from real-life problems and information objects, users interact with intentions, motivations and feelings, which can be seen as critical aspects of cognition and decision-making. The study presented in this paper serves as a starting point to the exploration of the role of emotions in the information seeking process. Results show that the latter not only interweave with different physiological, psychological and cognitive processes, but also form distinctive patterns, according to specific task, and according to specific user.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {395–402},
numpages = {8},
keywords = {affective interaction, relevance feedback, facial expression analysis},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390404,
author = {Radlinski, Filip and Broder, Andrei and Ciccolo, Peter and Gabrilovich, Evgeniy and Josifovski, Vanja and Riedel, Lance},
title = {Optimizing Relevance and Revenue in Ad Search: A Query Substitution Approach},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390404},
doi = {10.1145/1390334.1390404},
abstract = {The primary business model behind Web search is based on textual advertising, where contextually relevant ads are displayed alongside search results. We address the problem of selecting these ads so that they are both relevant to the queries and profitable to the search engine, showing that optimizing ad relevance and revenue is not equivalent. Selecting the best ads that satisfy these constraints also naturally incurs high computational costs, and time constraints can lead to reduced relevance and profitability. We propose a novel two-stage approach, which conducts most of the analysis ahead of time. An offine preprocessing phase leverages additional knowledge that is impractical to use in real time, and rewrites frequent queries in a way that subsequently facilitates fast and accurate online matching. Empirical evaluation shows that our method optimized for relevance matches a state-of-the-art method while improving expected revenue. When optimizing for revenue, we see even more substantial improvements in expected revenue.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {403–410},
numpages = {8},
keywords = {online advertising, revenue, relevance},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390405,
author = {Zhang, Min and Ye, Xingyao},
title = {A Generation Model to Unify Topic Relevance and Lexicon-Based Sentiment for Opinion Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390405},
doi = {10.1145/1390334.1390405},
abstract = {Opinion retrieval is a task of growing interest in social life and academic research, which is to find relevant and opinionate documents according to a user's query. One of the key issues is how to combine a document's opinionate score (the ranking score of to what extent it is subjective or objective) and topic relevance score. Current solutions to document ranking in opinion retrieval are generally ad-hoc linear combination, which is short of theoretical foundation and careful analysis. In this paper, we focus on lexicon-based opinion retrieval. A novel generation model that unifies topic-relevance and opinion generation by a quadratic combination is proposed in this paper. With this model, the relevance-based ranking serves as the weighting factor of the lexicon-based sentiment ranking function, which is essentially different from the popular heuristic linear combination approaches. The effect of different sentiment dictionaries is also discussed. Experimental results on TREC blog datasets show the significant effectiveness of the proposed unified model. Improvements of 28.1% and 40.3% have been obtained in terms of MAP and p@10 respectively. The conclusion is not limited to blog environment. Besides the unified generation model, another contribution is that our work demonstrates that in the opinion retrieval task, a Bayesian approach to combining multiple ranking functions is superior to using a linear combination. It is also applicable to other result re-ranking applications in similar scenario.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {411–418},
numpages = {8},
keywords = {sentiment analysis, topic relevance, opinion generation model, opinion retrieval, generation model},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390407,
author = {Wang, Mengqiu and Si, Luo},
title = {Discriminative Probabilistic Models for Passage Based Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390407},
doi = {10.1145/1390334.1390407},
abstract = {The approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with different characteristics. One main reason of the inconsistent performance is that there exists no unified framework to model the evidence of individual passages within a document. This paper proposes two probabilistic models to formally model the evidence of a set of top ranked passages in a document. The first probabilistic model follows the retrieval criterion that a document is relevant if any passage in the document is relevant, and models each passage independently. The second probabilistic model goes a step further and incorporates the similarity correlations among the passages. Both models are trained in a discriminative manner. Furthermore, we present a combination approach to combine the ranked lists of document retrieval and passage-based retrieval.An extensive set of experiments have been conducted on four different TREC test beds to show the effectiveness of the proposed discriminative probabilistic models for passage-based retrieval. The proposed algorithms are compared with a state-of-the-art document retrieval algorithm and a language model approach for passage-based retrieval. Furthermore, our combined approach has been shown to provide better results than both document retrieval and passage-based retrieval approaches.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {419–426},
numpages = {8},
keywords = {IR, discriminative models, passage retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390408,
author = {Xu, Zuobing and Akella, Ram},
title = {A New Probabilistic Retrieval Model Based on the Dirichlet Compound Multinomial Distribution},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390408},
doi = {10.1145/1390334.1390408},
abstract = {The classical probabilistic models attempt to capture the Ad hoc information retrieval problem within a rigorous probabilistic framework. It has long been recognized that the primary obstacle to effective performance of the probabilistic models is the need to estimate a relevance model. The Dirichlet compound multinomial (DCM) distribution, which relies on hierarchical Bayesian modeling techniques, or the Polya Urn scheme, is a more appropriate generative model than the traditional multinomial distribution for text documents. We explore a new probabilistic model based on the DCM distribution, which enables efficient retrieval and accurate ranking. Because the DCM distribution captures the dependency of repetitive word occurrences, the new probabilistic model is able to model the concavity of the score function more effectively. To avoid the empirical tuning of retrieval parameters, we design several parameter estimation algorithms to automatically set model parameters. Additionally, we propose a pseudo-relevance feedback algorithm based on the latent mixture modeling of the Dirichlet compound multinomial distribution to further improve retrieval accuracy. Finally, our experiments show that both the baseline probabilistic retrieval algorithm based on the DCM distribution and the corresponding pseudo-relevance feedback algorithm outperform the existing language modeling systems on several TREC retrieval tasks.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {427–434},
numpages = {8},
keywords = {language model, dirichlet distribution, multinomial distribution, probabilistic retrieval model},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390409,
author = {Roelleke, Thomas and Wang, Jun},
title = {TF-IDF Uncovered: A Study of Theories and Probabilities},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390409},
doi = {10.1145/1390334.1390409},
abstract = {Interpretations of TF-IDF are based on binary independence retrieval, Poisson, information theory, and language modelling. This paper contributes a review of existing interpretations, and then, TF-IDF is systematically related to the probabilities P(q|d) and P(d|q). Two approaches are explored: a space of independent, and a space of disjoint terms. For independent terms, an "extreme" query/non-query term assumption uncovers TF-IDF, and an analogy of P(d|q) and the probabilistic odds O(r|d, q) mirrors relevance feedback. For disjoint terms, a relationship between probability theory and TF-IDF is established through the integral + 1/x dx = log x. This study uncovers components such as divergence from randomness and pivoted document length to be inherent parts of a document-query independence (DQI) measure, and interestingly, an integral of the DQI over the term occurrence probability leads to TF-IDF.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {435–442},
numpages = {8},
keywords = {probability theory, TF-IDF interpretations, document-query-independence, integral, derivative of logarithm},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390411,
author = {Nie, Lan and Davison, Brian D.},
title = {Separate and Inequal: Preserving Heterogeneity in Topical Authority Flows},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390411},
doi = {10.1145/1390334.1390411},
abstract = {Web pages, like people, are often known by others in a variety of contexts. When those contexts are sufficiently distinct, a page's importance may be better represented by multiple domains of authority, rather than by one that indiscriminately mixes reputations. In this work we determine domains of authority by examining the contexts in which a page is cited. However, we find that it is not enough to determine separate domains of authority; our model additionally determines the local flow of authority based upon the relative similarity of the source and target authority domains. In this way, we differentiate both incoming and outgoing hyperlinks by topicality and importance rather than treating them indiscriminately. We find that this approach compares favorably to other topical ranking methods on two real-world datasets and produces an approximately 10% improvement in precision and quality of the top ten results over PageRank.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {443–450},
numpages = {8},
keywords = {web search engine, link analysis, PageRank, reputation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390412,
author = {Liu, Yuting and Gao, Bin and Liu, Tie-Yan and Zhang, Ying and Ma, Zhiming and He, Shuyuan and Li, Hang},
title = {BrowseRank: Letting Web Users Vote for Page Importance},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390412},
doi = {10.1145/1390334.1390412},
abstract = {This paper proposes a new method for computing page importance, referred to as BrowseRank. The conventional approach to compute page importance is to exploit the link graph of the web and to build a model based on that graph. For instance, PageRank is such an algorithm, which employs a discrete-time Markov process as the model. Unfortunately, the link graph might be incomplete and inaccurate with respect to data for determining page importance, because links can be easily added and deleted by web content creators. In this paper, we propose computing page importance by using a 'user browsing graph' created from user behavior data. In this graph, vertices represent pages and directed edges represent transitions between pages in the users' web browsing history. Furthermore, the lengths of staying time spent on the pages by users are also included. The user browsing graph is more reliable than the link graph for inferring page importance. This paper further proposes using the continuous-time Markov process on the user browsing graph as a model and computing the stationary probability distribution of the process as page importance. An efficient algorithm for this computation has also been devised. In this way, we can leverage hundreds of millions of users' implicit voting on page importance. Experimental results show that BrowseRank indeed outperforms the baseline methods such as PageRank and TrustRank in several tasks.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {451–458},
numpages = {8},
keywords = {user behavior data, continuous-time Markov process, PageRank, Q-matrix, user browsing graph},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390413,
author = {Wang, Yida and Yang, Jiang-Ming and Lai, Wei and Cai, Rui and Zhang, Lei and Ma, Wei-Ying},
title = {Exploring Traversal Strategy for Web Forum Crawling},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390413},
doi = {10.1145/1390334.1390413},
abstract = {In this paper, we study the problem of Web forum crawling. Web forum has now become an important data source of many Web applications; while forum crawling is still a challenging task due to complex in-site link structures and login controls of most forum sites. Without carefully selecting the traversal path, a generic crawler usually downloads many duplicate and invalid pages from forums, and thus wastes both the precious bandwidth and the limited storage space. To crawl forum data more effectively and efficiently, in this paper, we propose an automatic approach to exploring an appropriate traversal strategy to direct the crawling of a given target forum. In detail, the traversal strategy consists of the identification of the skeleton links and the detection of the page-flipping links. The skeleton links instruct the crawler to only crawl valuable pages and meanwhile avoid duplicate and uninformative ones; and the page-flipping links tell the crawler how to completely download a long discussion thread which is usually shown in multiple pages in Web forums. The extensive experimental results on several forums show encouraging performance of our approach. Following the discovered traversal strategy, our forum crawler can archive more informative pages in comparison with previous related work and a commercial generic crawler.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {459–466},
numpages = {8},
keywords = {forum crawler, sitemap, traversal strategy},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390415,
author = {Cong, Gao and Wang, Long and Lin, Chin-Yew and Song, Young-In and Sun, Yueheng},
title = {Finding Question-Answer Pairs from Online Forums},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390415},
doi = {10.1145/1390334.1390415},
abstract = {Online forums contain a huge amount of valuable user generated content. In this paper we address the problem of extracting question-answer pairs from forums. Question-answer pairs extracted from forums can be used to help Question Answering services (e.g. Yahoo! Answers) among other applications. We propose a sequential patterns based classification method to detect questions in a forum thread, and a graph based propagation method to detect answers for questions in the same thread. Experimental results show that our techniques are very promising.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {467–474},
numpages = {8},
keywords = {information extraction, graph based ranking, classification, question answering, labeled sequential patterns},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390416,
author = {Xue, Xiaobing and Jeon, Jiwoon and Croft, W. Bruce},
title = {Retrieval Models for Question and Answer Archives},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390416},
doi = {10.1145/1390334.1390416},
abstract = {Retrieval in a question and answer archive involves finding good answers for a user's question. In contrast to typical document retrieval, a retrieval model for this task can exploit question similarity as well as ranking the associated answers. In this paper, we propose a retrieval model that combines a translation-based language model for the question part with a query likelihood approach for the answer part. The proposed model incorporates word-to-word translation probabilities learned through exploiting different sources of information. Experiments show that the proposed translation based language model for the question part outperforms baseline methods significantly. By combining with the query likelihood language model for the answer part, substantial additional effectiveness improvements are obtained.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {475–482},
numpages = {8},
keywords = {question and answer retrieval, translation model, information retrieval, language model},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390417,
author = {Liu, Yandong and Bian, Jiang and Agichtein, Eugene},
title = {Predicting Information Seeker Satisfaction in Community Question Answering},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390417},
doi = {10.1145/1390334.1390417},
abstract = {Question answering communities such as Naver and Yahoo! Answers have emerged as popular, and often effective, means of information seeking on the web. By posting questions for other participants to answer, information seekers can obtain specific answers to their questions. Users of popular portals such as Yahoo! Answers already have submitted millions of questions and received hundreds of millions of answers from other participants. However, it may also take hours --and sometime days-- until a satisfactory answer is posted. In this paper we introduce the problem of predicting information seeker satisfaction in collaborative question answering communities, where we attempt to predict whether a question author will be satisfied with the answers submitted by the community participants. We present a general prediction model, and develop a variety of content, structure, and community-focused features for this task. Our experimental results, obtained from a largescale evaluation over thousands of real questions and user ratings, demonstrate the feasibility of modeling and predicting asker satisfaction. We complement our results with a thorough investigation of the interactions and information seeking patterns in question answering communities that correlate with information seeker satisfaction. Our models and predictions could be useful for a variety of applications such as user intent inference, answer ranking, interface design, and query suggestion and routing.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {483–490},
numpages = {8},
keywords = {information seeker satisfaction, community question answering},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390419,
author = {Bendersky, Michael and Croft, W. Bruce},
title = {Discovering Key Concepts in Verbose Queries},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390419},
doi = {10.1145/1390334.1390419},
abstract = {Current search engines do not, in general, perform well with longer, more verbose queries. One of the main issues in processing these queries is identifying the key concepts that will have the most impact on effectiveness. In this paper, we develop and evaluate a technique that uses query-dependent, corpus-dependent, and corpus-independent features for automatic extraction of key concepts from verbose queries. We show that our method achieves higher accuracy in the identification of key concepts than standard weighting methods such as inverse document frequency. Finally, we propose a probabilistic model for integrating the weighted key concepts identified by our method into a query, and demonstrate that this integration significantly improves retrieval effectiveness for a large set of natural language description queries derived from TREC topics on several newswire and web collections.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {491–498},
numpages = {8},
keywords = {verbose queries, key concepts extraction, information retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390420,
author = {Sanderson, Mark},
title = {Ambiguous Queries: Test Collections Need More Sense},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390420},
doi = {10.1145/1390334.1390420},
abstract = {Although there are many papers examining ambiguity in Information Retrieval, this paper shows that there is a whole class of ambiguous word that past research has barely explored. It is shown that the class is more ambiguous than other word types and is commonly used in queries. The lack of test collections containing ambiguous queries is highlighted and a method for creating collections from existing resources is described. Tests using the new collection show the impact of query ambiguity on an IR system: it is shown that conventional systems are incapable of dealing effectively with such queries and that current assumptions about how to improve search effectiveness do not hold when searching on this common query type.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {499–506},
numpages = {8},
keywords = {ambiguous queries, diversity, test collections},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390421,
author = {Welch, Michael J. and Cho, Junghoo},
title = {Automatically Identifying Localizable Queries},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390421},
doi = {10.1145/1390334.1390421},
abstract = {Personalization of web search results as a technique for improving user satisfaction has received notable attention in the research community over the past decade. Much of this work focuses on modeling and establishing a profile for each user to aid in personalization. Our work takes a more query-centric approach. In this paper, we present a method for efficient, automatic identification of a class of queries we define as localizable from a web search engine query log. We determine a set of relevant features and use conventional machine learning techniques to classify queries. Our experiments find that our technique is able to identify localizable queries with 94% accuracy.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {507–514},
numpages = {8},
keywords = {localizable query, web search, machine learning},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390423,
author = {Song, Yang and Zhuang, Ziming and Li, Huajing and Zhao, Qiankun and Li, Jia and Lee, Wang-Chien and Giles, C. Lee},
title = {Real-Time Automatic Tag Recommendation},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390423},
doi = {10.1145/1390334.1390423},
abstract = {Tags are user-generated labels for entities. Existing research on tag recommendation either focuses on improving its accuracy or on automating the process, while ignoring the efficiency issue. We propose a highly-automated novel framework for real-time tag recommendation. The tagged training documents are treated as triplets of (words, docs, tags), and represented in two bipartite graphs, which are partitioned into clusters by Spectral Recursive Embedding (SRE). Tags in each topical cluster are ranked by our novel ranking algorithm. A two-way Poisson Mixture Model (PMM) is proposed to model the document distribution into mixture components within each cluster and aggregate words into word clusters simultaneously. A new document is classified by the mixture model based on its posterior probabilities so that tags are recommended according to their ranks. Experiments on large-scale tagging datasets of scientific documents (CiteULike) and web pages del.icio.us) indicate that our framework is capable of making tag recommendation efficiently and effectively. The average tagging time for testing a document is around 1 second, with over 88% test documents correctly labeled with the top nine tags we suggested.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {515–522},
numpages = {8},
keywords = {tagging system, graph partitioning, mixture model},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390424,
author = {Schenkel, Ralf and Crecelius, Tom and Kacimi, Mouna and Michel, Sebastian and Neumann, Thomas and Parreira, Josiane X. and Weikum, Gerhard},
title = {Efficient Top-k Querying over Social-Tagging Networks},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390424},
doi = {10.1145/1390334.1390424},
abstract = {Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances.Queries for tag or keyword combinations that compute and rank the top-k results thus face a large variety of options that complicate the query processing and pose efficiency challenges. This paper addresses these issues by developing an incremental top-k algorithm with two-dimensional expansions: social expansion considers the strength of relations among users, and semantic expansion considers the relatedness of different tags. It presents a new algorithm, based on principles of threshold algorithms, by folding friends and related tags into the search space in an incremental on-demand manner. The excellent performance of the method is demonstrated by an experimental evaluation on three real-world datasets, crawled from deli.cio.us, Flickr, and LibraryThing.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {523–530},
numpages = {8},
keywords = {scoring and ranking, top-k processing, social networks},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390425,
author = {Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector},
title = {Social Tag Prediction},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390425},
doi = {10.1145/1390334.1390425},
abstract = {In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {531–538},
numpages = {8},
keywords = {association rules, social bookmarking, collaborative tagging, text classification},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390427,
author = {Liu, Ying and Li, Wenyuan and Lin, Yongjing and Jing, Liping},
title = {Spectral Geometry for Simultaneously Clustering and Ranking Query Search Results},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390427},
doi = {10.1145/1390334.1390427},
abstract = {How best to present query search results is an important problem in search engines and information retrieval systems. When a single query retrieves many results, simply showing them as a long list will provide users with poor overview. Nowadays, ranking and clustering query search results have been two useful separate post-processing techniques to organize retrieved documents. In this paper, we proposed a spectral analysis method based on the content similarity networks to integrate the clustering and ranking techniques for improving literature search. The new approach organizes all these search results into categories intelligently and simultaneously rank the results in each category. A variety of theoretical and empirical studies have demonstrated that the presented method performs well in real applications, especially in biomedical literature retrieval. Moreover, any free text information can be analyzed with the new method, i.e., the proposed approach can be applied to various information systems, such as Web search engines and literature search service.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {539–546},
numpages = {8},
keywords = {ranking, clustering, query search, spectral geometry},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390428,
author = {Kurland, Oren and Domshlak, Carmel},
title = {A Rank-Aggregation Approach to Searching for Optimal Query-Specific Clusters},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390428},
doi = {10.1145/1390334.1390428},
abstract = {To improve the precision at the very top ranks of a document list presented in response to a query, researchers suggested to exploit information induced from clustering of documents highly ranked by some initial search. We propose a novel model for ranking such (query-specific) clusters by the presumed percentage of relevant documents that they contain. The model is based on (i) proposing a palette of "witness" cluster properties that purportedly correlate with this percentage, (ii) devising concrete quantitative measures for these properties, and (iii) ordering the clusters via aggregation of rankings induced by these individual measures. Empirical evaluation shows that our model is consistently more effective than previously suggested methods in detecting clusters containing a high relevant-document percentage. Furthermore, the precision-at-top-ranks performance of this model transcends that of standard document-based retrieval, and competes with that of a state-of-the-art document-based retrieval approach.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {547–554},
numpages = {8},
keywords = {clusters, optimal cluster, rank aggregation, cluster properties, language models, query-specific clustering},
location = {Singapore, Singapore},
series = {SIGIR '08

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr

branch_name: refs/heads/main

extension: bib