text:
@inproceedings{10.1145/1148170.1148175,
author = {Agichtein, Eugene and Brill, Eric and Dumais, Susan and Ragno, Robert},
title = {Learning User Interaction Models for Predicting Web Search Result Preferences},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148175},
doi = {10.1145/1148170.1148175},
abstract = {Evaluating user preferences of web search results is crucial for search engine development, deployment, and maintenance. We present a real-world study of modeling the behavior of web search users to predict web search result preferences. Accurate modeling and interpretation of user behavior has important applications to ranking, click spam detection, web search personalization, and other tasks. Our key insight to improving robustness of interpreting implicit feedback is to model query-dependent deviations from the expected "noisy" user behavior. We show that our model of clickthrough interpretation improves prediction accuracy over state-of-the-art clickthrough methods. We generalize our approach to model user behavior beyond clickthrough, which results in higher preference prediction accuracy than models based on clickthrough information alone. We report results of a large-scale experimental evaluation that show substantial improvements over published implicit feedback interpretation methods.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {3–10},
numpages = {8},
keywords = {user behavior modeling, predicting relevance preferences, interpreting implicit relevance feedback},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148176,
author = {Turpin, Andrew and Scholer, Falk},
title = {User Performance versus Precision Measures for Simple Search Tasks},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148176},
doi = {10.1145/1148170.1148176},
abstract = {Several recent studies have demonstrated that the type of improvements in information retrieval system effectiveness reported in forums such as SIGIR and TREC do not translate into a benefit for users. Two of the studies used an instance recall task, and a third used a question answering task, so perhaps it is unsurprising that the precision based measures of IR system effectiveness on one-shot query evaluation do not correlate with user performance on these tasks. In this study, we evaluate two different information retrieval tasks on TREC Web-track data: a precision-based user task, measured by the length of time that users need to find a single document that is relevant to a TREC topic; and, a simple recall-based task, represented by the total number of relevant documents that users can identify within five minutes. Users employ search engines with controlled mean average precision (MAP) of between 55% and 95%. Our results show that there is no significant relationship between system effectiveness measured by MAP and the precision-based task. A significant, but weak relationship is present for the precision at one document returned metric. A weak relationship is present between MAP and the simple recall-based task.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {11–18},
numpages = {8},
keywords = {search engines, information retrieval evaluation, user study},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148177,
author = {Agichtein, Eugene and Brill, Eric and Dumais, Susan},
title = {Improving Web Search Ranking by Incorporating User Behavior Information},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148177},
doi = {10.1145/1148170.1148177},
abstract = {We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {19–26},
numpages = {8},
keywords = {web search ranking, web search, implicit relevance feedback},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148179,
author = {Minkov, Einat and Cohen, William W. and Ng, Andrew Y.},
title = {Contextual Search and Name Disambiguation in Email Using Graphs},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148179},
doi = {10.1145/1148170.1148179},
abstract = {Similarity measures for text have historically been an important tool for solving information retrieval problems. In many interesting settings, however, documents are often closely connected to other documents, as well as other non-textual objects: for instance, email messages are connected to other messages via header information. In this paper we consider extended similarity metrics for documents and other objects embedded in graphs, facilitated via a lazy graph walk. We provide a detailed instantiation of this framework for email data, where content, social networks and a timeline are integrated in a structural graph. The suggested framework is evaluated for two email-related problems: disambiguating names in email documents, and threading. We show that reranking schemes based on the graph-walk similarity measures often outperform baseline methods, and that further improvements can be obtained by use of appropriate learning methods.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {27–34},
numpages = {8},
keywords = {graph-based retrieval, threading, email, name disambiguation},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148180,
author = {Shen, Dou and Yang, Qiang and Sun, Jian-Tao and Chen, Zheng},
title = {Thread Detection in Dynamic Text Message Streams},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148180},
doi = {10.1145/1148170.1148180},
abstract = {Text message stream is a newly emerging type of Web data which is produced in enormous quantities with the popularity of Instant Messaging and Internet Relay Chat. It is beneficial for detecting the threads contained in the text stream for various applications, including information retrieval, expert recognition and even crime prevention. Despite its importance, not much research has been conducted so far on this problem due to the characteristics of the data in which the messages are usually very short and incomplete. In this paper, we present a stringent definition of the thread detection task and our preliminary solution to it. We propose three variations of a single-pass clustering algorithm for exploiting the temporal information in the streams. An algorithm based on linguistic features is also put forward to exploit the discourse structure information. We conducted several experiments to compare our approaches with some existing algorithms on a real dataset. The results show that all three variations of the single-pass algorithm outperform the basic single-pass algorithm. Our proposed algorithm based on linguistic features improves the performance relatively by 69.5% and 9.7% when compared with the basic single-pass algorithm and the best variation algorithm in terms of F1 respectively.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {35–42},
numpages = {8},
keywords = {thread detection, text stream, single-pass clustering, linguistic features},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148181,
author = {Balog, Krisztian and Azzopardi, Leif and de Rijke, Maarten},
title = {Formal Models for Expert Finding in Enterprise Corpora},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148181},
doi = {10.1145/1148170.1148181},
abstract = {Searching an organization's document repositories for experts provides a cost effective solution for the task of expert finding. We present two general strategies to expert searching given a document collection which are formalized using generative probabilistic models. The first of these directly models an expert's knowledge based on the documents that they are associated with, whilst the second locates documents on topic, and then finds the associated expert. Forming reliable associations is crucial to the performance of expert finding systems. Consequently, in our evaluation we compare the different approaches, exploring a variety of associations along with other operational parameters (such as topicality). Using the TREC Enterprise corpora, we show that the second strategy consistently outperforms the first. A comparison against other unsupervised techniques, reveals that our second model delivers excellent performance.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {43–50},
numpages = {8},
keywords = {expert finding, enterprise search},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148183,
author = {Mamou, Jonathan and Carmel, David and Hoory, Ron},
title = {Spoken Document Retrieval from Call-Center Conversations},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148183},
doi = {10.1145/1148170.1148183},
abstract = {We are interested in retrieving information from conversational speech corpora, such as call-center data. This data comprises spontaneous speech conversations with low recording quality, which makes automatic speech recognition (ASR) a highly difficult task. For typical call-center data, even state-of-the-art large vocabulary continuous speech recognition systems produce a transcript with word error rate of 30% or higher. In addition to the output transcript, advanced systems provide word confusion networks (WCNs), a compact representation of word lattices associating each word hypothesis with its posterior probability. Our work exploits the information provided by WCNs in order to improve retrieval performance. In this paper, we show that the mean average precision (MAP) is improved using WCNs compared to the raw word transcripts. Finally, we analyze the effect of increasing ASR word error rate on search effectiveness. We show that MAP is still reasonable even under extremely high error rate.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {51–58},
numpages = {8},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148184,
author = {Shen, Jialie and Cui, Bin and Shepherd, John and Tan, Kian-Lee},
title = {Towards Efficient Automated Singer Identification in Large Music Databases},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148184},
doi = {10.1145/1148170.1148184},
abstract = {Automated singer identification is important in organising, browsing and retrieving data in large music databases. In this paper, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for automated singer recognition. HSI can effectively use multiple low-level features extracted from both vocal and non-vocal music segments to enhance the identification process with a hybrid architecture and build profiles of individual singer characteristics based on statistical mixture models. Extensive experimental results conducted on a large music database demonstrate the superiority of our method over state-of-the-art approaches.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {59–66},
numpages = {8},
keywords = {music retrieval, singer identification},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148185,
author = {Maddage, Namunu C. and Li, Haizhou and Kankanhalli, Mohan S.},
title = {Music Structure Based Vector Space Retrieval},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148185},
doi = {10.1145/1148170.1148185},
abstract = {This paper proposes a novel framework for music content indexing and retrieval. The music structure information, i.e., timing, harmony and music region content, is represented by the layers of the music structure pyramid. We begin by extracting this layered structure information. We analyze the rhythm of the music and then segment the signal proportional to the inter-beat intervals. Thus, the timing information is incorporated in the segmentation process, which we call Beat Space Segmentation. To describe Harmony Events, we propose a two-layer hierarchical approach to model the music chords. We also model the progression of instrumental and vocal content as Acoustic Events. After information extraction, we propose a vector space modeling approach which uses these events as the indexing terms. In query-by-example music retrieval, a query is represented by a vector of the statistics of the n-gram events. We then propose two effective retrieval models, a hard-indexing scheme and a soft-indexing scheme. Experiments show that the vector space modeling is effective in representing the layered music information, achieving 82.5% top-5 retrieval accuracy using 15-sec music clips as the queries. The soft-indexing outperforms hard-indexing in general.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {67–74},
numpages = {8},
keywords = {acoustic event, n-gram, vector space modeling, music structure, harmony event, beat space segmentation},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148187,
author = {Feng, Guang and Liu, Tie-Yan and Wang, Ying and Bao, Ying and Ma, Zhiming and Zhang, Xu-Dong and Ma, Wei-Ying},
title = {AggregateRank: Bringing Order to Web Sites},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148187},
doi = {10.1145/1148170.1148187},
abstract = {Since the website is one of the most important organizational structures of the Web, how to effectively rank websites has been essential to many Web applications, such as Web search and crawling. In order to get the ranks of websites, researchers used to describe the inter-connectivity among websites with a so-called HostGraph in which the nodes denote websites and the edges denote linkages between websites (if and only if there are hyperlinks from the pages in one website to the pages in the other, there will be an edge between these two websites), and then adopted the random walk model in the HostGraph. However, as pointed in this paper, the random walk over such a HostGraph is not reasonable because it is not in accordance with the browsing behavior of web surfers. Therefore, the derivate rank cannot represent the true probability of visiting the corresponding website.In this work, we mathematically proved that the probability of visiting a website by the random web surfer should be equal to the sum of the PageRank values of the pages inside that website. Nevertheless, since the number of web pages is much larger than that of websites, it is not feasible to base the calculation of the ranks of websites on the calculation of PageRank. To tackle this problem, we proposed a novel method named AggregateRank rooted in the theory of stochastic complement, which cannot only approximate the sum of PageRank accurately, but also have a lower computational complexity than PageRank. Both theoretical analysis and experimental evaluation show that AggregateRank is a better method for ranking websites than previous methods.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {75–82},
numpages = {8},
keywords = {coupling matrix, stochastic complement theory, aggregateRank},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148188,
author = {Kurland, Oren and Lee, Lillian},
title = {Respect My Authority! HITS without Hyperlinks, Utilizing Cluster-Based Language Models},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148188},
doi = {10.1145/1148170.1148188},
abstract = {We present an approach to improving the precision of an initial document ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform reranking based on centrality within bipartite graphs of documents (on one side) and clusters (on the other side), on the premise that these are mutually reinforcing entities. Links between entities are created via consideration of language models induced from them.We find that our cluster-document graphs give rise to much better retrieval performance than previously proposed document-only graphs do. For example, authority-based reranking of documents via a HITS-style cluster-based approach outperforms a previously-proposed PageRank-inspired algorithm applied to solely-document graphs. Moreover, we also show that computing authority scores for clusters constitutes an effective method for identifying clusters containing a large percentage of relevant documents.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {83–90},
numpages = {8},
keywords = {high-accuracy retrieval, bipartite graph, hubs, cluster-based language models, HITS, graph-based retrieval, structural re-ranking, authorities, clusters, language modeling},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148189,
author = {Nie, Lan and Davison, Brian D. and Qi, Xiaoguang},
title = {Topical Link Analysis for Web Search},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148189},
doi = {10.1145/1148170.1148189},
abstract = {Traditional web link-based ranking schemes use a single score to measure a page's authority without concern of the community from which that authority is derived. As a result, a resource that is highly popular for one topic may dominate the results of another topic in which it is less authoritative. To address this problem, we suggest calculating a score vector for each page to distinguish the contribution from different topics, using a random walk model that probabilistically combines page topic distribution and link structure. We show how to incorporate the topical model within both PageRank and HITS without affecting the overall property and still render insight into topic-level transition. Experiments on multiple datasets indicate that our technique outperforms other ranking approaches that incorporate textual analysis.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {91–98},
numpages = {8},
keywords = {web search engine, pagerank, link analysis, HITS},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148191,
author = {Lin, Jimmy and Demner-Fushman, Dina},
title = {The Role of Knowledge in Conceptual Retrieval: A Study in the Domain of Clinical Medicine},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148191},
doi = {10.1145/1148170.1148191},
abstract = {Despite its intuitive appeal, the hypothesis that retrieval at the level of "concepts" should outperform purely term-based approaches remains unverified empirically. In addition, the use of "knowledge" has not consistently resulted in performance gains. After identifying possible reasons for previous negative results, we present a novel framework for "conceptual retrieval" that articulates the types of knowledge that are important for information seeking. We instantiate this general framework in the domain of clinical medicine based on the principles of evidence-based medicine (EBM). Experiments show that an EBM-based scoring algorithm dramatically outperforms a state-of-the-art baseline that employs only term statistics. Ablation studies further yield a better understanding of the performance contributions of different components. Finally, we discuss how other domains can benefit from knowledge-based approaches.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {99–106},
numpages = {8},
keywords = {semantic models, reranking, question answering},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148192,
author = {Roelleke, Thomas and Wang, Jun},
title = {A Parallel Derivation of Probabilistic Information Retrieval Models},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148192},
doi = {10.1145/1148170.1148192},
abstract = {This paper investigates in a stringent athematical formalism the parallel derivation of three grand probabilistic retrieval models: binary independent retrieval (BIR), Poisson model (PM), and language modelling (LM).The investigation has been motivated by a number of questions. Firstly, though sharing the same origin, namely the probability of relevance, the models differ with respect to event spaces. How can this be captured in a consistent notation, and can we relate the event spaces? Secondly, BIR and PM are closely related, but how does LM fit in? Thirdly, how are tf-idf and probabilistic models related? .The parallel investigation of the models leads to a number of formalised results: BIR and PM assume the collection to be a set of non-relevant documents, whereas LM assumes the collection to be a set of terms from relevant documents.PM can be viewed as a bridge connecting BIR and LM.A BIR-LM equivalence explains BIR as a special LM case.PM explains tf-idf, and both, BIR and LM probabilities express tf-idf in a dual way..},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {107–114},
numpages = {8},
keywords = {Poisson, binary retrieval model, language modelling},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148193,
author = {Fang, Hui and Zhai, ChengXiang},
title = {Semantic Term Matching in Axiomatic Approaches to Information Retrieval},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148193},
doi = {10.1145/1148170.1148193},
abstract = {A common limitation of many retrieval models, including the recently proposed axiomatic approaches, is that retrieval scores are solely based on exact (i.e., syntactic) matching of terms in the queries and documents, without allowing distinct but semantically related terms to match each other and contribute to the retrieval score. In this paper, we show that semantic term matching can be naturally incorporated into the axiomatic retrieval model through defining the primitive weighting function based on a semantic similarity function of terms. We define several desirable retrieval constraints for semantic term matching and use such constraints to extend the axiomatic model to directly support semantic term matching based on the mutual information of terms computed on some document set. We show that such extension can be efficiently implemented as query expansion. Experiment results on several representative data sets show that, with mutual information computed over the documents in either the target collection for retrieval or an external collection such as the Web, our semantic expansion consistently and substantially improves retrieval accuracy over the baseline axiomatic retrieval model. As a pseudo feedback method, our method also outperforms a state-of-the-art language modeling feedback method.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {115–122},
numpages = {8},
keywords = {retrieval heuristics, query expansion, feedback, constraints, axiomatic model},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148195,
author = {Lynam, Thomas R. and Cormack, Gordon V. and Cheriton, David R.},
title = {On-Line Spam Filter Fusion},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148195},
doi = {10.1145/1148170.1148195},
abstract = {We show that a set of independently developed spam filters may be combined in simple ways to provide substantially better filtering than any of the individual filters. The results of fifty-three spam filters evaluated at the TREC 2005 Spam Track were combined post-hoc so as to simulate the parallel on-line operation of the filters. The combined results were evaluated using the TREC methodology, yielding more than a factor of two improvement over the best filter. The simplest method -- averaging the binary classifications returned by the individual filters -- yields a remarkably good result. A new method -- averaging log-odds estimates based on the scores returned by the individual filters -- yields a somewhat better result, and provides input to SVM- and logistic-regression-based stacking methods. The stacking methods appear to provide further improvement, but only for very large corpora. Of the stacking methods, logistic regression yields the better result. Finally, we show that it is possible to select a priori small subsets of the filters that, when combined, still outperform the best individual filter by a substantial margin.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {123–130},
numpages = {8},
keywords = {email, classification, spam, filtering},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148196,
author = {Shen, Dou and Sun, Jian-Tao and Yang, Qiang and Chen, Zheng},
title = {Building Bridges for Web Query Classification},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148196},
doi = {10.1145/1148170.1148196},
abstract = {Web query classification (QC) aims to classify Web users' queries, which are often short and ambiguous, into a set of target categories. QC has many applications including page ranking in Web search, targeted advertisement in response to queries, and personalization. In this paper, we present a novel approach for QC that outperforms the winning solution of the ACM KDDCUP 2005 competition, whose objective is to classify 800,000 real user queries. In our approach, we first build a bridging classifier on an intermediate taxonomy in an offline mode. This classifier is then used in an online mode to map user queries to the target categories via the above intermediate taxonomy. A major innovation is that by leveraging the similarity distribution over the intermediate taxonomy, we do not need to retrain a new classifier for each new set of target categories, and therefore the bridging classifier needs to be trained only once. In addition, we introduce category selection as a new method for narrowing down the scope of the intermediate taxonomy based on which we classify the queries. Category selection can improve both efficiency and effectiveness of the online classification. By combining our algorithm with the winning solution of KDDCUP 2005, we made an improvement by 9.7% and 3.8% in terms of precision and F1 respectively compared with the best results of KDDCUP 2005.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {131–138},
numpages = {8},
keywords = {bridging classifier, KDDCUP 2005, web query classification, category selection},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148197,
author = {Lillis, David and Toolan, Fergus and Collier, Rem and Dunnion, John},
title = {ProbFuse: A Probabilistic Approach to Data Fusion},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148197},
doi = {10.1145/1148170.1148197},
abstract = {Data fusion is the combination of the results of independent searches on a document collection into one single output result set. It has been shown in the past that this can greatly improve retrieval effectiveness over that of the individual results.This paper presents probFuse, a probabilistic approach to data fusion. ProbFuse assumes that the performance of the individual input systems on a number of training queries is indicative of their future performance. The fused result set is based on probabilities of relevance calculated during this training process. Retrieval experiments using data from the TREC ad hoc collection demonstrate that probFuse achieves results superior to that of the popular CombMNZ fusion algorithm.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {139–146},
numpages = {8},
keywords = {information retrieval, ProbFuse, data fusion},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148199,
author = {Vassilvitskii, Sergei and Brill, Eric},
title = {Using Web-Graph Distance for Relevance Feedback in Web Search},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148199},
doi = {10.1145/1148170.1148199},
abstract = {We study the effect of user supplied relevance feedback in improving web search results. Rather than using query refinement or document similarity measures to rerank results, we show that the web-graph distance between two documents is a robust measure of their relative relevancy. We demonstrate how the use of this metric can improve the rankings of result URLs, even when the user only rates one document in the dataset. Our research suggests that such interactive systems can significantly improve search results.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {147–153},
numpages = {7},
keywords = {web search, link analysis, relevance feedback},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148200,
author = {Diaz, Fernando and Metzler, Donald},
title = {Improving the Estimation of Relevance Models Using Large External Corpora},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148200},
doi = {10.1145/1148170.1148200},
abstract = {Information retrieval algorithms leverage various collection statistics to improve performance. Because these statistics are often computed on a relatively small evaluation corpus, we believe using larger, non-evaluation corpora should improve performance. Specifically, we advocate incorporating external corpora based on language modeling. We refer to this process as external expansion. When compared to traditional pseudo-relevance feedback techniques, external expansion is more stable across topics and up to 10% more effective in terms of mean average precision. Our results show that using a high quality corpus that is comparable to the evaluation corpus can be as, if not more, effective than using the web. Our results also show that external expansion outperforms simulated relevance feedback. In addition, we propose a method for predicting the extent to which external expansion will improve retrieval performance. Our new measure demonstrates positive correlation with improvements in mean average precision.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {154–161},
numpages = {8},
keywords = {pseudo-relevance feedback, language models, relevance models, relevance feedback},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148201,
author = {Tao, Tao and Zhai, ChengXiang},
title = {Regularized Estimation of Mixture Models for Robust Pseudo-Relevance Feedback},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148201},
doi = {10.1145/1148170.1148201},
abstract = {Pseudo-relevance feedback has proven to be an effective strategy for improving retrieval accuracy in all retrieval models. However the performance of existing pseudo feedback methods is often affected significantly by some parameters, such as the number of feedback documents to use and the relative weight of original query terms; these parameters generally have to be set by trial-and-error without any guidance. In this paper, we present a more robust method for pseudo feedback based on statistical language models. Our main idea is to integrate the original query with feedback documents in a single probabilistic mixture model and regularize the estimation of the language model parameters in the model so that the information in the feedback documents can be gradually added to the original query. Unlike most existing feedback methods, our new method has no parameter to tune. Experiment results on two representative data sets show that the new method is significantly more robust than a state-of-the-art baseline language modeling approach for feedback with comparable or better retrieval accuracy.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {162–169},
numpages = {8},
keywords = {pseudo feedback, EM, regulation, mixture model},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148203,
author = {Zhou, Xiaohua and Hu, Xiaohua and Zhang, Xiaodan and Lin, Xia and Song, Il-Yeol},
title = {Context-Sensitive Semantic Smoothing for the Language Modeling Approach to Genomic IR},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148203},
doi = {10.1145/1148170.1148203},
abstract = {Semantic smoothing, which incorporates synonym and sense information into the language models, is effective and potentially significant to improve retrieval performance. The implemented semantic smoothing models, such as the translation model which statistically maps document terms to query terms, and a number of works that have followed have shown good experimental results. However, these models are unable to incorporate contextual information. Thus, the resulting translation might be mixed and fairly general. To overcome this limitation, we propose a novel context-sensitive semantic smoothing method that decomposes a document or a query into a set of weighted context-sensitive topic signatures and then translate those topic signatures into query terms. In detail, we solve this problem through (1) choosing concept pairs as topic signatures and adopting an ontology-based approach to extract concept pairs; (2) estimating the translation model for each topic signature using the EM algorithm; and (3) expanding document and query models based on topic signature translations. The new smoothing method is evaluated on TREC 2004/05 Genomics Track collections and significant improvements are obtained. The MAP (mean average precision) achieves a 33.6% maximal gain over the simple language model, as well as a 7.8% gain over the language model with context-insensitive semantic smoothing.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {170–177},
numpages = {8},
keywords = {information retrieval, language models, topic signature, semantic smoothing, genomic information retrieval, concept pair},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148204,
author = {Wei, Xing and Croft, W. Bruce},
title = {LDA-Based Document Models for Ad-Hoc Retrieval},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148204},
doi = {10.1145/1148170.1148204},
abstract = {Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {178–185},
numpages = {8},
keywords = {information retrieval, latent dirichlet allocation (LDA), language model, topic model, document model},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148205,
author = {Cao, Yunbo and Xu, Jun and Liu, Tie-Yan and Li, Hang and Huang, Yalou and Hon, Hsiao-Wuen},
title = {Adapting Ranking SVM to Document Retrieval},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148205},
doi = {10.1145/1148170.1148205},
abstract = {The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a "learning to rank" method, to document retrieval. First, correctly ranking documents on the top of the result list is crucial for an Information Retrieval system. One must conduct training in a way that such ranked results are accurate. Second, the number of relevant documents can vary from query to query. One must avoid training a model biased toward queries with a large number of relevant documents. Previously, when existing methods that include Ranking SVM were applied to document retrieval, none of the two factors was taken into consideration. We show it is possible to make modifications in conventional Ranking SVM, so it can be better used for document retrieval. Specifically, we modify the "Hinge Loss" function in Ranking SVM to deal with the problems described above. We employ two methods to conduct optimization on the loss function: gradient descent and quadratic programming. Experimental results show that our method, referred to as Ranking SVM for IR, can outperform the conventional Ranking SVM and other existing methods for document retrieval on two datasets.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {186–193},
numpages = {8},
keywords = {information retrieval, loss function, ranking SVM},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148207,
author = {Gao, Jianfeng and Nie, Jian-Yun},
title = {A Study of Statistical Models for Query Translation: Finding a Good Unit of Translation},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148207},
doi = {10.1145/1148170.1148207},
abstract = {This paper presents a study of three statistical query translation models that use different units of translation. We begin with a review of a word-based translation model that uses co-occurrence statistics for resolving translation ambiguities. The translation selection problem is then formulated under the framework of graphic model resorting to which the modeling assumptions and limitations of the co-occurrence model are discussed, and the research of finding better translation units is motivated. Then, two other models that use larger, linguistically motivated translation units (i.e., noun phrase and dependency triple) are presented. For each model, the modeling and training methods are described in detail. All query translation models are evaluated using TREC collections. Results show that larger translation units lead to more specific models that usually achieve better translation and cross-language information retrieval results.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {194–201},
numpages = {8},
keywords = {cross-language information retrieval, query translation, statistical models, linguistic structures},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148208,
author = {Wang, Jianqiang and Oard, Douglas W.},
title = {Combining Bidirectional Translation and Synonymy for Cross-Language Information Retrieval},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148208},
doi = {10.1145/1148170.1148208},
abstract = {This paper introduces a general framework for the use of translation probabilities in cross-language information retrieval based on the notion that information retrieval fundamentally requires matching what the searcher means with what the author of a document meant. That perspective yields a computational formulation that provides a natural way of combining what have been known as query and document translation. Two well-recognized techniques are shown to be a special case of this model under restrictive assumptions. Cross-language search results are reported that are statistically indistinguishable from strong monolingual baselines for both French and Chinese documents.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {202–209},
numpages = {8},
keywords = {statistical translation, cross-language IR},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148210,
author = {Han, Kyoung-Soo and Song, Young-In and Rim, Hae-Chang},
title = {Probabilistic Model for Definitional Question Answering},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148210},
doi = {10.1145/1148170.1148210},
abstract = {This paper proposes a probabilistic model for definitional question answering (QA) that reflects the characteristics of the definitional question. The intention of the definitional question is to request the definition about the question target. Therefore, an answer for the definitional question should contain the content relevant to the topic of the target, and have a representation form of the definition style. Modeling the problem of definitional QA from both the topic and definition viewpoints, the proposed probabilistic model converts the task of answering the definitional questions into that of estimating the three language models: topic language model, definition language model, and general language model. The proposed model systematically combines several evidences in a probabilistic framework. Experimental results show that a definitional QA system based on the proposed probabilistic model is comparable to state-of-the-art systems.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {212–219},
numpages = {8},
keywords = {language model, definitional question answering, probabilistic model},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148211,
author = {Harabagiu, Sanda and Lacatusu, Finley and Hickl, Andrew},
title = {Answering Complex Questions with Random Walk Models},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148211},
doi = {10.1145/1148170.1148211},
abstract = {We present a novel framework for answering complex questions that relies on question decomposition. Complex questions are decomposed by a procedure that operates on a Markov chain, by following a random walk on a bipartite graph of relations established between concepts related to the topic of a complex question and subquestions derived from topic-relevant passages that manifest these relations. Decomposed questions discovered during this random walk are then submitted to a state-of-the-art Question Answering (Q/A) system in order to retrieve a set of passages that can later be merged into a comprehensive answer by a Multi-Document Summarization (MDS) system. In our evaluations, we show that access to the decompositions generated using this method can significantly enhance the relevance and comprehensiveness of summary-length answers to complex questions.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {220–227},
numpages = {8},
keywords = {question answering, summarization},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148212,
author = {Jeon, Jiwoon and Croft, W. Bruce and Lee, Joon Ho and Park, Soyeon},
title = {A Framework to Predict the Quality of Answers with Non-Textual Features},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148212},
doi = {10.1145/1148170.1148212},
abstract = {New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to predict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a significant improvement over our baseline.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {228–235},
numpages = {8},
keywords = {information retrieval, document quality, maximum entropy, language models},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148214,
author = {Wang, Xuanhui and Sun, Jian-Tao and Chen, Zheng and Zhai, ChengXiang},
title = {Latent Semantic Analysis for Multiple-Type Interrelated Data Objects},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148214},
doi = {10.1145/1148170.1148214},
abstract = {Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment results show that M-LSA outperforms LSA on multiple applications, including collaborative filtering, text clustering, and text categorization.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {236–243},
numpages = {8},
keywords = {LSA, mutual reinforcement principle, M-LSA, multiple-type},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148215,
author = {Jindal, Nitin and Liu, Bing},
title = {Identifying Comparative Sentences in Text Documents},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148215},
doi = {10.1145/1148170.1148215},
abstract = {This paper studies the problem of identifying comparative sentences in text documents. The problem is related to but quite different from sentiment/opinion sentence identification or classification. Sentiment classification studies the problem of classifying a document or a sentence based on the subjective opinion of the author. An important application area of sentiment/opinion identification is business intelligence as a product manufacturer always wants to know consumers' opinions on its products. Comparisons on the other hand can be subjective or objective. Furthermore, a comparison is not concerned with an object in isolation. Instead, it compares the object with others. An example opinion sentence is "the sound quality of CD player X is poor". An example comparative sentence is "the sound quality of CD player X is not as good as that of CD player Y". Clearly, these two sentences give different information. Their language constructs are quite different too. Identifying comparative sentences is also useful in practice because direct comparisons are perhaps one of the most convincing ways of evaluation, which may even be more important than opinions on each individual object. This paper proposes to study the comparative sentence identification problem. It first categorizes comparative sentences into different types, and then presents a novel integrated pattern discovery and supervised learning approach to identifying comparative sentences from text documents. Experiment results using three types of documents, news articles, consumer reviews of products, and Internet forum postings, show a precision of 79% and recall of 81%. More detailed results are given in the paper.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {244–251},
numpages = {8},
keywords = {comparative sentences, text mining, sentiment classification},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148216,
author = {Forman, George},
title = {Tackling Concept Drift by Temporal Inductive Transfer},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148216},
doi = {10.1145/1148170.1148216},
abstract = {Machine learning is the mainstay for text classification. However, even the most successful techniques are defeated by many real-world applications that have a strong time-varying component. To advance research on this challenging but important problem, we promote a natural, experimental framework-the Daily Classification Task-which can be applied to large time-based datasets, such as Reuters RCV1.In this paper we dissect concept drift into three main subtypes. We demonstrate via a novel visualization that the recurrent themes subtype is present in RCV1. This understanding led us to develop a new learning model that transfers induced knowledge through time to benefit future classifier learning tasks. The method avoids two main problems with existing work in inductive transfer: scalability and the risk of negative transfer. In empirical tests, it consistently showed more than 10 points F-measure improvement for each of four Reuters categories tested.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {252–259},
numpages = {8},
keywords = {machine learning, inductive transfer, support vector machine, time series, concept drift, text classification, topic identification},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148218,
author = {Piwowarski, Benjamin and Dupret, Georges},
title = {Evaluation in (XML) Information Retrieval: Expected Precision-Recall with User Modelling (EPRUM)},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148218},
doi = {10.1145/1148170.1148218},
abstract = {Standard Information Retrieval (IR) metrics assume a simple model where documents are understood as independent units. Such an assumption is not adapted to new paradigms like XML or Web IR where retrievable informations are parts of documents or sets of related documents. Moreover, classical hypotheses assumes that the user ignores the structural or logical context of document elements and hence the possibility of navigation between units. EPRUM is a generalisation of Precision-Recall (PR) that aims at allowing the user to navigate or browse in the corpus structure. Like the Cumulated Gain metrics, it is able to handle continuous valued relevance. We apply and compare EPRUM in the context of XML Retrieval -- a very active field for evaluation metrics. We also explain how EPRUM can be used in other IR paradigms.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {260–267},
numpages = {8},
keywords = {recall-precision, web retrieval, XML retrieval, metric, passage retrieval, evaluation},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148219,
author = {Carterette, Ben and Allan, James and Sitaraman, Ramesh},
title = {Minimal Test Collections for Retrieval Evaluation},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148219},
doi = {10.1145/1148170.1148219},
abstract = {Accurate estimation of information retrieval evaluation metrics such as average precision require large sets of relevance judgments. Building sets large enough for evaluation of real-world implementations is at best inefficient, at worst infeasible. In this work we link evaluation with test collection construction to gain an understanding of the minimal judging effort that must be done to have high confidence in the outcome of an evaluation. A new way of looking at average precision leads to a natural algorithm for selecting documents to judge and allows us to estimate the degree of confidence by defining a distribution over possible document judgments. A study with annotators shows that this method can be used by a small group of researchers to rank a set of systems in under three hours with 95% confidence. Information retrieval metrics such as average precision require large sets of relevance judgments to be accurately estimated. Building these sets is infeasible and often inefficient for many real-world retrieval implementations. We present a new way of looking at average precision that allows us to estimate the confidence in an evaluation based on the size of the test collection. We use this to build an algorithm for selecting the best documents to judge to have maximum confidence in an evaluation with a minimal number of relevance judgments. A study with annotators shows how the algorithm can be used by a small group of researchers to quickly rank a set of systems with 95% confidence.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {268–275},
numpages = {8},
keywords = {test collections, evaluation, information retrieval, theory, algorithms},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148220,
author = {Soboroff, Ian},
title = {Dynamic Test Collections: Measuring Search Effectiveness on the Live Web},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148220},
doi = {10.1145/1148170.1148220},
abstract = {Existing methods for measuring the quality of search algorithms use a static collection of documents. A set of queries and a mapping from the queries to the relevant documents allow the experimenter to see how well different search engines or engine configurations retrieve the correct answers. This methodology assumes that the document set and thus the set of relevant documents are unchanging. In this paper, we abandon the static collection requirement. We begin with a recent TREC collection created from a web crawl and analyze how the documents in that collection have changed over time. We determine how decay of the document collection affects TREC systems, and present the results of an experiment using the decayed collection to measure a live web search system. We employ novel measures of search effectiveness that are robust despite incomplete relevance information. Lastly, we propose a methodology of "collection maintenance" which supports measuring search performance both for a single system and between systems run at different points in time.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {276–283},
numpages = {8},
keywords = {retrieval test collections},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148222,
author = {Henzinger, Monika},
title = {Finding Near-Duplicate Web Pages: A Large-Scale Evaluation of Algorithms},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148222},
doi = {10.1145/1148170.1148222},
abstract = {Broder et al.'s [3] shingling algorithm and Charikar's [4] random projection based approach are considered "state-of-the-art" algorithms for finding near-duplicate web pages. Both algorithms were either developed at or used by popular web search engines. We compare the two algorithms on a very large scale, namely on a set of 1.6B distinct web pages. The results show that neither of the algorithms works well for finding near-duplicate pairs on the same site, while both achieve high precision for near-duplicate pairs on different sites. Since Charikar's algorithm finds more near-duplicate pairs on different sites, it achieves a better precision overall, namely 0.50 versus 0.38 for Broder et al.'s algorithm. We present a combined algorithm which achieves precision 0.79 with 79% of the recall of the other algorithms.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {284–291},
numpages = {8},
keywords = {near-duplicate documents, web pages, content duplication},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148223,
author = {Vidal, M\'{a}rcio L. A. and da Silva, Altigran S. and de Moura, Edleno S. and Cavalcanti, Jo\~{a}o M. B.},
title = {Structure-Driven Crawler Generation by Example},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148223},
doi = {10.1145/1148170.1148223},
abstract = {Many Web IR and Digital Library applications require a crawling process to collect pages with the ultimate goal of taking advantage of useful information available on Web sites. For some of these applications the criteria to determine when a page is to be present in a collection are related to the page content. However, there are situations in which the inner structure of the pages provides a better criteria to guide the crawling process than their content. In this paper, we present a structure-driven approach for generating Web crawlers that requires a minimum effort from users. The idea is to take as input a sample page and an entry point to a Web site and generate a structure-driven crawler based on navigation patterns, sequences of patterns for the links a crawler has to follow to reach the pages structurally similar to the sample page. In the experiments we have carried out, structure-driven crawlers generated by our new approach were able to collect all pages that match the samples given, including those pages added after their generation.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {292–299},
numpages = {8},
keywords = {web crawlers, digital libraries, tree edit distance},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148224,
author = {Xu, Gu and Ma, Wei-Ying},
title = {Building Implicit Links from Content for Forum Search},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148224},
doi = {10.1145/1148170.1148224},
abstract = {The objective of Web forums is to create a shared space for open communications and discussions of specific topics and issues. The tremendous information behind forum sites is not fully-utilized yet. Most links between forum pages are automatically created, which means the link-based ranking algorithm cannot be applied efficiently. In this paper, we proposed a novel ranking algorithm which tries to introduce the content information into link-based methods as implicit links. The basic idea is derived from the more focused random surfer: the surfer may more likely jump to a page which is similar to what he is reading currently. In this manner, we are allowed to introduce the content similarities into the link graph as a personalization bias. Our method, named Fine-grained Rank (FGRank), can be efficiently computed based on an automatically generated topic hierarchy. Not like the topic-sensitive PageRank, our method only need to compute single PageRank score for each page. Another contribution of this paper is to present a very efficient algorithm for automatically generating topic hierarchy and map each page in a large-scale collection onto the computed hierarchy. The experimental results show that the proposed method can improve retrieval performance, and reveal that content-based link graph is also important compared with the hyper-link graph.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {300–307},
numpages = {8},
keywords = {clustering, PageRank, hierarchy generation, forum search, categorization},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148225,
author = {Baeza-Yates, Ricardo and Boldi, Paolo and Castillo, Carlos},
title = {Generalizing PageRank: Damping Functions for Link-Based Ranking Algorithms},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148225},
doi = {10.1145/1148170.1148225},
abstract = {This paper introduces a family of link-based ranking algorithms that propagate page importance through links. In these algorithms there is a damping function that decreases with distance, so a direct link implies more endorsement than a link through a long path. PageRank is the most widely known ranking function of this family.The main objective of this paper is to determine whether this family of ranking techniques has some interest per se, and how different choices for the damping function impact on rank quality and on convergence speed. Even though our results suggest that PageRank can be approximated with other simpler forms of rankings that may be computed more efficiently, our focus is of more speculative nature, in that it aims at separating the kernel of PageRank, that is, link-based importance propagation, from the way propagation decays over paths.We focus on three damping functions, having linear, exponential, and hyperbolic decay on the lengths of the paths. The exponential decay corresponds to PageRank, and the other functions are new. Our presentation includes algorithms, analysis, comparisons and experiments that study their behavior under different parameters in real Web graph data.Among other results, we show how to calculate a linear approximation that induces a page ordering that is almost identical to PageRank's using a fixed small number of iterations; comparisons were performed using Kendall's τ on large domain datasets.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {308–315},
numpages = {8},
keywords = {link analysis, web graphs, link-based ranking},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148227,
author = {Shokouhi, Milad and Zobel, Justin and Scholer, Falk and Tahaghoghi, S. M. M.},
title = {Capturing Collection Size for Distributed Non-Cooperative Retrieval},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148227},
doi = {10.1145/1148170.1148227},
abstract = {Modern distributed information retrieval techniques require accurate knowledge of collection size. In non-cooperative environments, where detailed collection statistics are not available, the size of the underlying collections must be estimated. While several approaches for the estimation of collection size have been proposed, their accuracy has not been thoroughly evaluated. An empirical analysis of past estimation approaches across a variety of collections demonstrates that their prediction accuracy is low. Motivated by ecological techniques for the estimation of animal populations, we propose two new approaches for the estimation of collection size. We show that our approaches are significantly more accurate that previous methods, and are more efficient in use of resources required to perform the estimation.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {316–323},
numpages = {8},
keywords = {capture-history, capture-recapture, collection size estimation, sample-resample},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148228,
author = {Yan, Rong and Hauptmann, Alexander G.},
title = {Probabilistic Latent Query Analysis for Combining Multiple Retrieval Sources},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148228},
doi = {10.1145/1148170.1148228},
abstract = {Combining the output from multiple retrieval sources over the same document collection is of great importance to a number of retrieval tasks such as multimedia retrieval, web retrieval and meta-search. To merge retrieval sources adaptively according to query topics, we propose a series of new approaches called probabilistic latent query analysis (pLQA), which can associate non-identical combination weights with latent classes underlying the query space. Compared with previous query independent and query-class based combination methods, the proposed approaches have the advantage of being able to discover latent query classes automatically without using prior human knowledge, to assign one query to a mixture of query classes, and to determine the number of query classes under a model selection principle. Experimental results on two retrieval tasks, i.e., multimedia retrieval and meta-search, demonstrate that the proposed methods can uncover sensible latent classes from training data, and can achieve considerable performance gains.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {324–331},
numpages = {8},
keywords = {combination, statistical learning, query class, retrieval},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148229,
author = {Lu, Jie and Callan, Jamie},
title = {User Modeling for Full-Text Federated Search in Peer-to-Peer Networks},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148229},
doi = {10.1145/1148170.1148229},
abstract = {User modeling for information retrieval has mostly been studied to improve the effectiveness of information access in centralized repositories. In this paper we explore user modeling in the context of full-text federated search in peer-to-peer networks. Our approach models a user's persistent, long-term interests based on past queries, and uses the model to improve search efficiency for future queries that represent interests similar to past queries. Our approach also enables queries representing a user's transient, ad-hoc interests to be automatically recognized so that search for these queries can rely on a relatively large search radius to avoid sacrificing effectiveness for efficiency. Experimental results demonstrate that our approach can significantly improve the efficiency of full-text federated search without degrading its accuracy. Furthermore, the proposed approach does not require a large amount of training data, and is robust to a range of parameter values.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {332–339},
numpages = {8},
keywords = {user modeling, federated search, peer-to-peer, query clustering},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148230,
author = {Caverlee, James and Liu, Ling and Bae, Joonsoo},
title = {Distributed Query Sampling: A Quality-Conscious Approach},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148230},
doi = {10.1145/1148170.1148230},
abstract = {We present an adaptive distributed query-sampling framework that is quality-conscious for extracting high-quality text database samples. The framework divides the query-based sampling process into an initial seed sampling phase and a quality-aware iterative sampling phase. In the second phase the sampling process is dynamically scheduled based on estimated database size and quality parameters derived during the previous sampling process. The unique characteristic of our adaptive query-based sampling framework is its self-learning and self-configuring ability based on the overall quality of all text databases under consideration. We introduce three quality-conscious sampling schemes for estimating database quality, and our initial results show that the proposed framework supports higher-quality document sampling than existing approaches.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {340–347},
numpages = {8},
keywords = {quality, sampling, distributed IR, adaptive},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148232,
author = {Moffat, Alistair and Webber, William and Zobel, Justin},
title = {Load Balancing for Term-Distributed Parallel Retrieval},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148232},
doi = {10.1145/1148170.1148232},
abstract = {Large-scale web and text retrieval systems deal with amounts of data that greatly exceed the capacity of any single machine. To handle the necessary data volumes and query throughput rates, parallel systems are used, in which the document and index data are split across tightly-clustered distributed computing systems. The index data can be distributed either by document or by term. In this paper we examine methods for load balancing in term-distributed parallel architectures, and propose a suite of techniques for reducing net querying costs. In combination, the techniques we describe allow a 30% improvement in query throughput when tested on an eight-node parallel computer system.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {348–355},
numpages = {8},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148233,
author = {B\"{u}ttcher, Stefan and Clarke, Charles L. A. and Lushman, Brad},
title = {Hybrid Index Maintenance for Growing Text Collections},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148233},
doi = {10.1145/1148170.1148233},
abstract = {We present a new family of hybrid index maintenance strategies to be used in on-line index construction for monotonically growing text collections. These new strategies improve upon recent results for hybrid index maintenance in dynamic text retrieval systems. Like previous techniques, our new method distinguishes between short and long posting lists: While short lists are maintained using a merge strategy, long lists are kept separate and are updated in-place. This way, costly relocations of long posting lists are avoided.We discuss the shortcomings of previous hybrid methods and give an experimental evaluation of the new technique, showing that its index maintenance performance is superior to that of the earlier methods, especially when the amount of main memory available to the indexing system is small. We also present a complexity analysis which proves that, under a Zipfian term distribution, the asymptotical number of disk accesses performed by the best hybrid maintenance strategy is linear in the size of the text collection, implying the asymptotical optimality of the proposed strategy.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {356–363},
numpages = {8},
keywords = {in-place, merge, hybrid, information retrieval, index maintenance, index construction},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148234,
author = {Bast, Holger and Weber, Ingmar},
title = {Type Less, Find More: Fast Autocompletion Search with a Succinct Index},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148234},
doi = {10.1145/1148170.1148234},
abstract = {We consider the following full-text search autocompletion feature. Imagine a user of a search engine typing a query. Then with every letter being typed, we would like an instant display of completions of the last query word which would lead to good hits. At the same time, the best hits for any of these completions should be displayed. Known indexing data structures that apply to this problem either incur large processing times for a substantial class of queries, or they use a lot of space. We present a new indexing data structure that uses no more space than a state-of-the-art compressed inverted index, but with 10 times faster query processing times. Even on the large TREC Terabyte collection, which comprises over 25 million documents, we achieve, on a single machine and with the index on disk, average response times of one tenth of a second. We have built a full-fledged, interactive search engine that realizes the proposed autocompletion feature combined with support for proximity search, semi-structured (XML) text, subword and phrase completion, and semantic tags.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {364–371},
numpages = {8},
keywords = {index data structure, autocompletion, empirical entropy},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148235,
author = {Anh, Vo Ngoc and Moffat, Alistair},
title = {Pruned Query Evaluation Using Pre-Computed Impacts},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148235},
doi = {10.1145/1148170.1148235},
abstract = {Exhaustive evaluation of ranked queries can be expensive, particularly when only a small subset of the overall ranking is required, or when queries contain common terms. This concern gives rise to techniques for dynamic query pruning, that is, methods for eliminating redundant parts of the usual exhaustive evaluation, yet still generating a demonstrably "good enough" set of answers to the query. In this work we propose new pruning methods that make use of impact-sorted indexes. Compared to exhaustive evaluation, the new methods reduce the amount of computation performed, reduce the amount of memory required for accumulators, reduce the amount of data transferred from disk, and at the same time allow performance guarantees in terms of precision and mean average precision. These strong claims are backed by experiments using the TREC Terabyte collection and queries.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {372–379},
numpages = {8},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148237,
author = {Sun, Renxu and Ong, Chai-Huat and Chua, Tat-Seng},
title = {Mining Dependency Relations for Query Expansion in Passage Retrieval},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148237},
doi = {10.1145/1148170.1148237},
abstract = {Classical query expansion techniques such as the local context analysis (LCA) make use of term co-occurrence statistics to incorporate additional contextual terms for enhancing passage retrieval. However, relevant contextual terms do not always co-occur frequently with the query terms and vice versa. Hence the use of such methods often brings in noise, which leads to reduced precision. Previous studies have demonstrated the importance of relationship analysis for natural language queries in passage retrieval. However, they found that without query expansion, the performance is not satisfactory for short queries. In this paper, we present two novel query expansion techniques that make use of dependency relation analysis to extract contextual terms and relations from external corpuses. The techniques are used to enhance the performance of density based and relation based passage retrieval frameworks respectively. We compare the performance of the resulting systems with LCA in a density based passage retrieval system (DBS) and a relation based system without any query expansion (RBS) using the factoid questions from the TREC-12 QA task. The results show that in terms of MRR scores, our relation based term expansion method with DBS outperforms the LCA by 9.81%, while our relation expansion method outperforms RBS by 17.49%.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {382–389},
numpages = {8},
keywords = {passage retrieval, dependency parsing, query expansion},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148238,
author = {Carmel, David and Yom-Tov, Elad and Darlow, Adam and Pelleg, Dan},
title = {What Makes a Query Difficult?},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148238},
doi = {10.1145/1148170.1148238},
abstract = {This work tries to answer the question of what makes a query difficult. It addresses a novel model that captures the main components of a topic and the relationship between those components and topic difficulty. The three components of a topic are the textual expression describing the information need (the query or queries), the set of documents relevant to the topic (the Qrels), and the entire collection of documents. We show experimentally that topic difficulty strongly depends on the distances between these components. In the absence of knowledge about one of the model components, the model is still useful by approximating the missing component based on the other components. We demonstrate the applicability of the difficulty model for several uses such as predicting query difficulty, predicting the number of topic aspects expected to be covered by the search results, and analyzing the findability of a specific domain.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {390–397},
numpages = {8},
keywords = {query difficulty},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148239,
author = {Vinay, Vishwa and Cox, Ingemar J. and Milic-Frayling, Natasa and Wood, Ken},
title = {On Ranking the Effectiveness of Searches},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148239},
doi = {10.1145/1148170.1148239},
abstract = {There is a growing interest in estimating the effectiveness of search. Two approaches are typically considered: examining the search queries and examining the retrieved document sets. In this paper, we take the latter approach. We use four measures to characterize the retrieved document sets and estimate the quality of search. These measures are (i) the clustering tendency as measured by the Cox-Lewis statistic, (ii) the sensitivity to document perturbation, (iii) the sensitivity to query perturbation and (iv) the local intrinsic dimensionality. We present experimental results for the task of ranking 200 queries according to the search effectiveness over the TREC (discs 4 and 5) dataset. Our ranking of queries is compared with the ranking based on the average precision using the Kendall t statistic. The best individual estimator is the sensitivity to document perturbation and yields Kendall t of 0.521. When combined with the clustering tendency based on the Cox-Lewis statistic and the query perturbation measure, it results in Kendall t of 0.562 which to our knowledge is the highest correlation with the average precision reported to date.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {398–404},
numpages = {7},
keywords = {query performance prediction},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148241,
author = {Ji, Xiang and Xu, Wei},
title = {Document Clustering with Prior Knowledge},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148241},
doi = {10.1145/1148170.1148241},
abstract = {Document clustering is an important tool for text analysis and is used in many different applications. We propose to incorporate prior knowledge of cluster membership for document cluster analysis and develop a novel semi-supervised document clustering model. The method models a set of documents with weighted graph in which each document is represented as a vertex, and each edge connecting a pair of vertices is weighted with the similarity value of the two corresponding documents. The prior knowledge indicates pairs of documents that known to belong to the same cluster. Then, the prior knowledge is transformed into a set of constraints. The document clustering task is accomplished by finding the best cuts of the graph under the constraints. We apply the model to the Normalized Cut method to demonstrate the idea and concept. Our experimental evaluations show that the proposed document clustering model reveals remarkable performance improvements with very limited training samples, and hence is a very effective semi-supervised classification tool.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {405–412},
numpages = {8},
keywords = {clustering with prior knowledge, semi-supervised learning, spectral clustering},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148242,
author = {Huang, Yifen and Mitchell, Tom M.},
title = {Text Clustering with Extended User Feedback},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148242},
doi = {10.1145/1148170.1148242},
abstract = {Text clustering is most commonly treated as a fully automated task without user feedback. However, a variety of researchers have explored mixed-initiative clustering methods which allow a user to interact with and advise the clustering algorithm. This mixed-initiative approach is especially attractive for text clustering tasks where the user is trying to organize a corpus of documents into clusters for some particular purpose (e.g., clustering their email into folders that reflect various activities in which they are involved). This paper introduces a new approach to mixed-initiative clustering that handles several natural types of user feedback. We first introduce a new probabilistic generative model for text clustering (the SpeClustering model) and show that it outperforms the commonly used mixture of multinomials clustering model, even when used in fully autonomous mode with no user input. We then describe how to incorporate four distinct types of user feedback into the clustering algorithm, and provide experimental evidence showing substantial improvements in text clustering when this user feedback is incorporated.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {413–420},
numpages = {8},
keywords = {mixed-initiative learning, text clustering, user feedback},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148243,
author = {Yang, Hui and Callan, Jamie},
title = {Near-Duplicate Detection by Instance-Level Constrained Clustering},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148243},
doi = {10.1145/1148170.1148243},
abstract = {For the task of near-duplicated document detection, both traditional fingerprinting techniques used in database community and bag-of-word comparison approaches used in information retrieval community are not sufficiently accurate. This is due to the fact that the characteristics of near-duplicated documents are different from that of both "almost-identical" documents in the data cleaning task and "relevant" documents in the search task. This paper presents an instance-level constrained clustering approach for near-duplicate detection. The framework incorporates information such as document attributes and content structure into the clustering process to form near-duplicate clusters. Gathered from several collections of public comments sent to U.S. government agencies on proposed new regulations, the experimental results demonstrate that our approach outperforms other near-duplicate detection algorithms and as about as effective as human assessors.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {421–428},
numpages = {8},
keywords = {duplicate detection, public comments, clustering},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148245,
author = {Chen, Harr and Karger, David R.},
title = {Less is More: Probabilistic Models for Retrieving Fewer Relevant Documents},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148245},
doi = {10.1145/1148170.1148245},
abstract = {Traditionally, information retrieval systems aim to maximize the
number of relevant documents returned to a user within some window
of the top. For that goal, the probability ranking principle, which
ranks documents in decreasing order of probability of relevance, is
provably optimal. However, there are many scenarios in which that
ranking does not optimize for the users information need. One
example is when the user would be satisfied with some limited
number of relevant documents, rather than needing all relevant
documents. We show that in such a scenario, an attempt to return
many relevant documents can actually reduce the chances of finding
any relevant documents.We consider a number of information retrieval metrics from the
literature, including the rank of the first relevant result, the
%no metric that penalizes a system only for retrieving no relevant
results near the top, and the diversity of retrieved results when
queries have multiple interpretations. We observe that given a
probabilistic model of relevance, it is appropriate to rank so as
to directly optimize these metrics in expectation. While doing so
may be computationally intractable, we show that a simple greedy
optimization algorithm that approximately optimizes the given
objectives produces rankings for TREC queries that outperform the
standard approach based on the probability ranking principle.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {429–436},
numpages = {8},
keywords = {formal models, subtopic retrieval, information retrieval, machine learning},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148246,
author = {Matveeva, Irina and Burges, Chris and Burkard, Timo and Laucius, Andy and Wong, Leon},
title = {High Accuracy Retrieval with Multiple Nested Ranker},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148246},
doi = {10.1145/1148170.1148246},
abstract = {High precision at the top ranks has become a new focus of research in information retrieval. This paper presents the multiple nested ranker approach that improves the accuracy at the top ranks by iteratively re-ranking the top scoring documents. At each iteration, this approach uses the RankNet learning algorithm to re-rank a subset of the results. This splits the problem into smaller and easier tasks and generates a new distribution of the results to be learned by the algorithm. We evaluate this approach using different settings on a data set labeled with several degrees of relevance. We use the normalized discounted cumulative gain (NDCG) to measure the performance because it depends not only on the position but also on the relevance score of the document in the ranked list. Our experiments show that making the learning algorithm concentrate on the top scoring results improves precision at the top ten documents in terms of the NDCG score.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {437–444},
numpages = {8},
keywords = {re-ranking, high accuracy retrieval, ad-hoc retrieval},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148247,
author = {Chu-Carroll, Jennifer and Prager, John and Czuba, Krzysztof and Ferrucci, David and Duboue, Pablo},
title = {Semantic Search via XML Fragments: A High-Precision Approach to IR},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148247},
doi = {10.1145/1148170.1148247},
abstract = {In some IR applications, it is desirable to adopt a high precision search strategy to return a small set of documents that are highly focused and relevant to the user's information need. With these applications in mind, we investigate semantic search using the XML Fragments query language on text corpora automatically pre-processed to encode semantic information useful for retrieval. We identify three XML Fragment operations that can be applied to a query to conceptualize, restrict, or relate terms in the query. We demonstrate how these operations can be used to address four different query-time semantic needs: to specify target information type, to disambiguate keywords, to specify search term context, or to relate select terms in the query. We demonstrate the effectiveness of our semantic search technology through a series of experiments using the two applications in which we embed this technology and show that it yields significant improvement in precision in the search results.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {445–452},
numpages = {8},
keywords = {XML retrieval, semantic search, question answering},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148249,
author = {Kelly, Diane and Fu, Xin},
title = {Elicitation of Term Relevance Feedback: An Investigation of Term Source and Context},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148249},
doi = {10.1145/1148170.1148249},
abstract = {Term relevance feedback has had a long history in information retrieval. However, research on interactive term relevance feedback has yielded mixed results. In this paper, we investigate several aspects related to the elicitation of term relevance feedback: the display of document surrogates, the technique for identifying or selecting terms, and sources of expansion terms. We conduct a between subjects experiment (n=61) of three term relevance feedback interfaces using the 2005 TREC HARD collection, and evaluate each interface with respect to query length and retrieval performance. Results demonstrate that queries created with each experimental interface significantly outperformed corresponding baseline queries, even though there were no differences in performance between interface conditions. Results also demonstrate that pseudo-relevance feedback runs outperformed both baseline and experimental runs as assessed by recall-oriented measures, but that user-generated terms improved precision.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {453–460},
numpages = {8},
keywords = {query expansion, query length, elicitation of feedback, term context, relevance feedback interfaces, familiarity, user feedback},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148250,
author = {Smucker, Mark D. and Allan, James},
title = {Find-Similar: Similarity Browsing as a Search Tool},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148250},
doi = {10.1145/1148170.1148250},
abstract = {Search systems have for some time provided users with the ability to request documents similar to a given document. Interfaces provide this feature via a link or button for each document in the search results. We call this feature find-similar or similarity browsing. We examined find-similar as a search tool, like relevance feedback, for improving retrieval performance. Our investigation focused on find-similar's document-to-document similarity, the reexamination of documents during a search, and the user's browsing pattern. Find-similar with a query-biased similarity, avoiding the reexamination of documents, and a breadth-like browsing pattern achieved a 23% increase in the arithmetic mean average precision and a 66% increase in the geometric mean average precision over our baseline retrieval. This performance matched that of a more traditionally styled iterative relevance feedback technique.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {461–468},
numpages = {8},
keywords = {relevance feedback, find-similar, similarity browsing},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148251,
author = {Lin, Jimmy and Wu, Philip and Demner-Fushman, Dina and Abels, Eileen},
title = {Exploring the Limits of Single-Iteration Clarification Dialogs},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148251},
doi = {10.1145/1148170.1148251},
abstract = {Single-iteration clarification dialogs, as implemented in the TREC HARD track, represent an attempt to introduce interaction into ad hoc retrieval, while preserving the many benefits of large-scale evaluations. Although previous experiments have not conclusively demonstrated performance gains resulting from such interactions, it is unclear whether these findings speak to the nature of clarification dialogs, or simply the limitations of current systems. To probe the limits of such interactions, we employed a human intermediary to formulate clarification questions and exploit user responses. In addition to establishing a plausible upper bound on performance, we were also able to induce an "ontology of clarifications" to characterize human behavior. This ontology, in turn, serves as the input to a regression model that attempts to determine which types of clarification questions are most helpful. Our work can serve to inform the design of interactive systems that initiate user dialogs.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {469–476},
numpages = {8},
keywords = {interactive retrieval, intermediated search, TREC HARD},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148253,
author = {Sindhwani, Vikas and Keerthi, S. Sathiya},
title = {Large Scale Semi-Supervised Linear SVMs},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148253},
doi = {10.1145/1148170.1148253},
abstract = {Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In many information retrieval and data mining applications, linear classifiers are strongly preferred because of their ease of implementation, interpretability and empirical performance. In this work, we present a family of semi-supervised linear support vector classifiers that are designed to handle partially-labeled sparse datasets with possibly very large number of examples and features. At their core, our algorithms employ recently developed modified finite Newton techniques. Our contributions in this paper are as follows: (a) We provide an implementation of Transductive SVM (TSVM) that is significantly more efficient and scalable than currently used dual techniques, for linear classification problems involving large, sparse datasets. (b) We propose a variant of TSVM that involves multiple switching of labels. Experimental results show that this variant provides an order of magnitude further improvement in training efficiency. (c) We present a new algorithm for semi-supervised learning based on a Deterministic Annealing (DA) approach. This algorithm alleviates the problem of local minimum in the TSVM optimization procedure while also being computationally attractive. We conduct an empirical study on several document classification tasks which confirms the value of our methods in large scale semi-supervised settings.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {477–484},
numpages = {8},
keywords = {support vector machines, unlabeled data, global optimization, text categorization},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148254,
author = {Angelova, Ralitsa and Weikum, Gerhard},
title = {Graph-Based Text Classification: Learn from Your Neighbors},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148254},
doi = {10.1145/1148170.1148254},
abstract = {Automatic classification of data items, based on training samples, can be boosted by considering the neighborhood of data items in a graph structure (e.g., neighboring documents in a hyperlink environment or co-authors and their publications for bibliographic data entries). This paper presents a new method for graph-based classification, with particular emphasis on hyperlinked text documents but broader applicability. Our approach is based on iterative relaxation labeling and can be combined with either Bayesian or SVM classifiers on the feature spaces of the given data items. The graph neighborhood is taken into consideration to exploit locality patterns while at the same time avoiding overfitting. In contrast to prior work along these lines, our approach employs a number of novel techniques: dynamically inferring the link/class pattern in the graph in the run of the iterative relaxation labeling, judicious pruning of edges from the neighborhood graph based on node dissimilarities and node degrees, weighting the influence of edges based on a distance metric between the classification labels of interest and weighting edges by content similarity measures. Our techniques considerably improve the robustness and accuracy of the classification outcome, as shown in systematic experimental comparisons with previously published methods on three different real-world datasets.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {485–492},
numpages = {8},
keywords = {automatic classification, exploiting link structure},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148255,
author = {Dayanik, Aynur and Lewis, David D. and Madigan, David and Menkov, Vladimir and Genkin, Alexander},
title = {Constructing Informative Prior Distributions from Domain Knowledge in Text Classification},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148255},
doi = {10.1145/1148170.1148255},
abstract = {Supervised learning approaches to text classification are in practice often required to work with small and unsystematically collected training sets. The alternative to supervised learning is usually viewed to be building classifiers by hand, using a domain expert's understanding of which features of the text are related to the class of interest. This is expensive, requires a degree of sophistication about linguistics and classification, and makes it difficult to use combinations of weak predictors. We propose instead combining domain knowledge with training examples in a Bayesian framework. Domain knowledge is used to specify a prior distribution for the parameters of a logistic regression model, and labeled training data is used to produce a posterior distribution, whose mode we take as the final classifier. We show on three text categorization data sets that this approach can rescue what would otherwise be disastrously bad training situations, producing much more effective classifiers.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {493–500},
numpages = {8},
keywords = {maximum entropy, MAP estimation, knowledge-based},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148257,
author = {Wang, Jun and de Vries, Arjen P. and Reinders, Marcel J. T.},
title = {Unifying User-Based and Item-Based Collaborative Filtering Approaches by Similarity Fusion},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148257},
doi = {10.1145/1148170.1148257},
abstract = {Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large number of ratings from similar users or similar items are not available, due to the sparsity inherent to rating data. Consequently, prediction quality can be poor. This paper re-formulates the memory-based collaborative filtering problem in a generative probabilistic framework, treating individual user-item ratings as predictors of missing ratings. The final rating is estimated by fusing predictions from three sources: predictions based on ratings of the same item by other users, predictions based on different item ratings made by the same user, and, third, ratings predicted based on data from other but similar users rating other but similar items. Existing user-based and item-based approaches correspond to the two simple cases of our framework. The complete model is however more robust to data sparsity, because the different types of ratings are used in concert, while additional ratings from similar users towards similar items are employed as a background model to smooth the predictions. Experiments demonstrate that the proposed methods are indeed more robust against data sparsity and give better recommendations.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {501–508},
numpages = {8},
keywords = {similarity fusion, smoothing, recommender systems, collaborative filtering},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148258,
author = {Song, Xiaodan and Tseng, Belle L. and Lin, Ching-Yung and Sun, Ming-Ting},
title = {Personalized Recommendation Driven by Information Flow},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148258},
doi = {10.1145/1148170.1148258},
abstract = {We propose that the information access behavior of a group of people can be modeled as an information flow issue, in which people intentionally or unintentionally influence and inspire each other, thus creating an interest in retrieving or getting a specific kind of information or product. Information flow models how information is propagated in a social network. It can be a real social network where interactions between people reside; it can be, moreover, a virtual social network in that people only influence each other unintentionally, for instance, through collaborative filtering. We leverage users' access patterns to model information flow and generate effective personalized recommendations. First, an early adoption based information flow (EABIF) network describes the influential relationships between people. Second, based on the fact that adoption is typically category specific, we propose a topic-sensitive EABIF (TEABIF) network, in which access patterns are clustered with respect to the categories. Once an item has been accessed by early adopters, personalized recommendations are achieved by estimating whom the information will be propagated to with high probabilities. In our experiments with an online document recommendation system, the results demonstrate that the EABIF and the TEABIF can respectively achieve an improved (precision, recall) of (91.0%, 87.1%) and (108.5%, 112.8%) compared to traditional collaborative filtering, given an early adopter exists.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {509–516},
numpages = {8},
keywords = {information flow, collaborative filtering, personalized recommendation},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148259,
author = {Zhang, Sheng and Ouyang, Yi and Ford, James and Makedon, Fillia},
title = {Analysis of a Low-Dimensional Linear Model under Recommendation Attacks},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148259},
doi = {10.1145/1148170.1148259},
abstract = {Collaborative filtering techniques have become popular in the past decade as an effective way to help people deal with information overload. Recent research has identified significant vulnerabilities in collaborative filtering techniques. Shilling attacks, in which attackers introduce biased ratings to influence recommendation systems, have been shown to be effective against memory-based collaborative filtering algorithms. We examine the effectiveness of two popular shilling attacks (the random attack and the average attack) on a model-based algorithm that uses Singular Value Decomposition (SVD) to learn a low-dimensional linear model. Our results show that the SVD-based algorithm is much more resistant to shilling attacks than memory-based algorithms. Furthermore, we develop an attack detection method directly built on the SVD-based algorithm and show that this method detects random shilling attacks with high detection rates and very low false alarm rates.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {517–524},
numpages = {8},
keywords = {recommender systems, anomaly detection, shilling attacks, collaborative filtering},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148261,
author = {Sakai, Tetsuya},
title = {Evaluating Evaluation Metrics Based on the Bootstrap},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148261},
doi = {10.1145/1148170.1148261},
abstract = {This paper describes how the Bootstrap approach to statistics can be applied to the evaluation of IR effectiveness metrics. First, we argue that Bootstrap Hypothesis Tests deserve more attention from the IR community, as they are based on fewer assumptions than traditional statistical significance tests. We then describe straightforward methods for comparing the sensitivity of IR metrics based on Bootstrap Hypothesis Tests. Unlike the heuristics-based "swap" method proposed by Voorhees and Buckley, our method estimates the performance difference required to achieve a given significance level directly from Bootstrap Hypothesis Test results. In addition, we describe a simple way of examining the accuracy of rank correlation between two metrics based on the Bootstrap Estimate of Standard Error. We demonstrate the usefulness of our methods using test collections and runs from the NTCIR CLIR track for comparing seven IR metrics, including those that can handle graded relevance and those based on the Geometric Mean.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {525–532},
numpages = {8},
keywords = {test collection, bootstrap, evaluation, graded relevance},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148262,
author = {Cormack, Gordon V. and Lynam, Thomas R.},
title = {Statistical Precision of Information Retrieval Evaluation},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148262},
doi = {10.1145/1148170.1148262},
abstract = {We introduce and validate bootstrap techniques to compute confidence intervals that quantify the effect of test-collection variability on average precision (AP) and mean average precision (MAP) IR effectiveness measures. We consider the test collection in IR evaluation to be a representative of a population of materially similar collections, whose documents are drawn from an infinite pool with similar characteristics. Our model accurately predicts the degree of concordance between system results on randomly selected halves of the TREC-6 ad hoc corpus. We advance a framework for statistical evaluation that uses the same general framework to model other sources of chance variation as a source of input for meta-analysis techniques.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {533–540},
numpages = {8},
keywords = {precision, bootstrap, confidence interval},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148263,
author = {Aslam, Javed A. and Pavlu, Virgil and Yilmaz, Emine},
title = {A Statistical Method for System Evaluation Using Incomplete Judgments},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148263},
doi = {10.1145/1148170.1148263},
abstract = {We consider the problem of large-scale retrieval evaluation, and we propose a statistical method for evaluating retrieval systems using incomplete judgments. Unlike existing techniques that (1) rely on effectively complete, and thus prohibitively expensive, relevance judgment sets, (2) produce biased estimates of standard performance measures, or (3) produce estimates of non-standard measures thought to be correlated with these standard measures, our proposed statistical technique produces unbiased estimates of the standard measures themselves.Our proposed technique is based on random sampling. While our estimates are unbiased by statistical design, their variance is dependent on the sampling distribution employed; as such, we derive a sampling distribution likely to yield low variance estimates. We test our proposed technique using benchmark TREC data, demonstrating that a sampling pool derived from a set of runs can be used to efficiently and effectively evaluate those runs. We further show that these sampling pools generalize well to unseen runs. Our experiments indicate that highly accurate estimates of standard performance measures can be obtained using a number of relevance judgments as small as 4% of the typical TREC-style judgment pool.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {541–548},
numpages = {8},
keywords = {evaluation, sampling, average precision},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148265,
author = {Lacerda, An\'{\i}sio and Cristo, Marco and Gon\c{c}alves, Marcos Andr\'{e} and Fan, Weiguo and Ziviani, Nivio and Ribeiro-Neto, Berthier},
title = {Learning to Advertise},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148265},
doi = {10.1145/1148170.1148265},
abstract = {Content-targeted advertising, the task of automatically associating ads to a Web page, constitutes a key Web monetization strategy nowadays. Further, it introduces new challenging technical problems and raises interesting questions. For instance, how to design ranking functions able to satisfy conflicting goals such as selecting advertisements (ads) that are relevant to the users and suitable and profitable to the publishers and advertisers? In this paper we propose a new framework for associating ads with web pages based on Genetic Programming (GP). Our GP method aims at learning functions that select the most appropriate ads, given the contents of a Web page. These ranking functions are designed to optimize overall precision and minimize the number of misplacements. By using a real ad collection and web pages from a newspaper, we obtained a gain over a state-of-the-art baseline method of 61.7% in average precision. Further, by evolving individuals to provide good ranking estimations, GP was able to discover ranking functions that are very effective in placing ads in web pages while avoiding irrelevant ones.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {549–556},
numpages = {8},
keywords = {genetic programming, web advertising},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148266,
author = {Li, Yunyao and Krishnamurthy, Rajasekar and Vaithyanathan, Shivakumar and Jagadish, H. V.},
title = {Getting Work Done on the Web: Supporting Transactional Queries},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148266},
doi = {10.1145/1148170.1148266},
abstract = {Many searches on the web have a transactional intent. We argue that pages satisfying transactional needs can be distinguished from the more common pages that have some information and links, but cannot be used to execute a transaction. Based on this hypothesis, we provide a recipe for constructing a transaction annotator. By constructing an annotator with one corpus and then demonstrating its classification performance on another,we establish its robustness. Finally, we show experimentally that a search procedure that exploits such pre-annotation greatly outperforms traditional search for retrieving transactional pages.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {557–564},
numpages = {8},
keywords = {information extraction, transactional search, intranet search},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@inproceedings{10.1145/1148170.1148267,
author = {Frankowski, Dan and Cosley, Dan and Sen, Shilad and Terveen, Loren and Riedl, John},
title = {You Are What You Say: Privacy Risks of Public Mentions},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148267},
doi = {10.1145/1148170.1148267},
abstract = {In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated.},
booktitle = {Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {565–572},
numpages = {8},
keywords = {k-identification, mentions, re-identification, k-anonymity, datasets, sparse relation space, privacy},
location = {Seattle, Washington, USA},
series = {SIGIR '06}
}

@dataset{10.1145/review-1148170.1148267_R41529,
author = {Clapes, Anthony L.},
title = {Review ID:R41529 for DOI: 10.1145/1148170.1148267},
publisher = {Association for Computing Machinery},
url = {https://doi.org/10.1145/review-1148170.1148267_R41529}
}

@inproceedings{10.1145/1148170.1148269,
author = {Nenkova, Ani and Vanderwende, Lucy and McKeown, Kathleen},
title = {A Compositional Context Sensitive Multi-Document Summarizer: Exploring the Factors That Influence Summarization},
year = {2006},
isbn = {1595933697},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1148170.1148269},
doi = {10.1145/1148170.1148269},
abstract = {The usual approach for automatic summarization is sentence extraction, where key sentences from the input documents are selected based on a suite of features. While word frequency often is used as a feature in summarization, its impact on system performance has not been isolated. In this paper, we study the contribution to summarization of three factors related to frequency: content word frequency, composition functions for estimating sentence importance from word frequency, and adjustment of frequency weights based on context. We carry out our analysis using datasets from the Document Understanding Conferences, studying not only the impact of these features on automatic summarizers, but also their role in human summarization. Our research shows that a frequency based summarizer can achieve performance comparable to that of

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr

branch_name: refs/heads/main