text:
ClueWeb12 are still representative of those that would work directly on industry-strength web search engines. Do we need to sacrifice reproducibility for the sake of representativeness? We study the difference in effectiveness between Open Web systems and ClueWeb12 systems through analyzing the relevance assessments of documents identified from both the Open Web and ClueWeb12. Then, we identify documents that overlap between the relevance assessments of the Open Web and ClueWeb12, observing a dependency between relevance assessments and the source of the document being taken from the Open Web or from ClueWeb12. After that, we identify documents from the relevance assessments of the Open Web which exist in the ClueWeb12 collection but do not exist in the ClueWeb12 relevance assessments. We use these documents to expand the ClueWeb12 relevance assessments. Our main findings are twofold. First, our empirical analysis of the relevance assessments of 2 years of CS track shows that Open Web documents receive better ratings than ClueWeb12 documents, especially if we look at the documents in the overlap. Second, our approach for selecting candidate documents from ClueWeb12 collection based on information obtained from the Open Web makes an improvement step towards partially bridging the gap in effectiveness between Open Web and ClueWeb12 systems, while at the same time we achieve reproducible results on well-known representative sample of the web.},
journal = {Inf. Retr.},
month = jun,
pages = {230–255},
numpages = {26},
keywords = {Contextual suggestion, Test collections evaluation, Filtering and recommendation, Web IR and social media search, Open vs archived web, Reproducibility}
}

@article{10.1007/s10791-015-9275-x,
author = {Demeester, Thomas and Aly, Robin and Hiemstra, Djoerd and Nguyen, Dong and Develder, Chris},
title = {Predicting Relevance Based on Assessor Disagreement: Analysis and Practical Applications for Search Evaluation},
year = {2016},
issue_date = {June      2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9275-x},
doi = {10.1007/s10791-015-9275-x},
abstract = {Evaluation of search engines relies on assessments of search results for selected test queries, from which we would ideally like to draw conclusions in terms of relevance of the results for general (e.g., future, unknown) users. In practice however, most evaluation scenarios only allow us to conclusively determine the relevance towards the particular assessor that provided the judgments. A factor that cannot be ignored when extending conclusions made from assessors towards users, is the possible disagreement on relevance, assuming that a single gold truth label does not exist. This paper presents and analyzes the predicted relevance model (PRM), which allows predicting a particular result's relevance for a random user, based on an observed assessment and knowledge on the average disagreement between assessors. With the PRM, existing evaluation metrics designed to measure binary assessor relevance, can be transformed into more robust and effectively graded measures that evaluate relevance towards a random user. It also leads to a principled way of quantifying multiple graded or categorical relevance levels for use as gains in established graded relevance measures, such as normalized discounted cumulative gain, which nowadays often use heuristic and data-independent gain values. Given a set of test topics with graded relevance judgments, the PRM allows evaluating systems on different scenarios, such as their capability of retrieving top results, or how well they are able to filter out non-relevant ones. Its use in actual evaluation scenarios is illustrated on several information retrieval test collections.},
journal = {Inf. Retr.},
month = jun,
pages = {284–312},
numpages = {29},
keywords = {Graded relevance assessments for information retrieval, Information retrieval evaluation, Test collections, Assessor disagreement}
}

@article{10.1007/s10791-015-9274-y,
author = {Urbano, Juli\'{a}n},
title = {Test Collection Reliability: A Study of Bias and Robustness to Statistical Assumptions via Stochastic Simulation},
year = {2016},
issue_date = {June      2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9274-y},
doi = {10.1007/s10791-015-9274-y},
abstract = {The number of topics that a test collection contains has a direct impact on how well the evaluation results reflect the true performance of systems. However, large collections can be prohibitively expensive, so researchers are bound to balance reliability and cost. This issue arises when researchers have an existing collection and they would like to know how much they can trust their results, and also when they are building a new collection and they would like to know how many topics it should contain before they can trust the results. Several measures have been proposed in the literature to quantify the accuracy of a collection to estimate the true scores, as well as different ways to estimate the expected accuracy of hypothetical collections with a certain number of topics. We can find ad-hoc measures such as Kendall tau correlation and swap rates, and statistical measures such as statistical power and indexes from generalizability theory. Each measure focuses on different aspects of evaluation, has a different theoretical basis, and makes a number of assumptions that are not met in practice, such as normality of distributions, homoscedasticity, uncorrelated effects and random sampling. However, how good these estimates are in practice remains a largely open question. In this paper we first compare measures and estimators of test collection accuracy and propose unbiased statistical estimators of the Kendall tau and tau AP correlation coefficients. Second, we detail a method for stochastic simulation of evaluation results under different statistical assumptions, which can be used for a variety of evaluation research where we need to know the true scores of systems. Third, through large-scale simulation from TREC data, we analyze the bias of a range of estimators of test collection accuracy. Fourth, we analyze the robustness to statistical assumptions of these estimators, in order to understand what aspects of an evaluation are affected by what assumptions and guide in the development of new collections and new measures. All the results in this paper are fully reproducible with data and code available online.},
journal = {Inf. Retr.},
month = jun,
pages = {313–350},
numpages = {38},
keywords = {Information retrieval, Evaluation, Test collection, Simulation, Reliability}
}

@article{10.1007/s10791-015-9273-z,
author = {Sakai, Tetsuya},
title = {Topic Set Size Design},
year = {2016},
issue_date = {June      2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9273-z},
doi = {10.1007/s10791-015-9273-z},
abstract = {Traditional pooling-based information retrieval (IR) test collections typically have $$n= 50$$n=50---100 topics, but it is difficult for an IR researcher to say why the topic set size should really be n.
 The present study provides details on principled ways to determine the number of topics for a test collection to be built, based on a specific set of statistical requirements. We employ Nagata's three sample size design techniques, which are based on the paired t test, one-way ANOVA, and confidence intervals, respectively. These topic set size design methods require topic-by-run score matrices from past test collections for the purpose of estimating the within-system population variance for a particular evaluation measure. While the previous work of Sakai incorrectly used estimates of the total variances, here we use the correct estimates of the within-system variances, which yield slightly smaller topic set sizes than those reported previously by Sakai. Moreover, this study provides a comparison across the three methods. Our conclusions nevertheless echo those of Sakai: as different evaluation measures can have vastly different within-system variances, they require substantially different topic set sizes under the same set of statistical requirements; by analysing the tradeoff between the topic set size and the pool depth for a particular evaluation measure in advance, researchers can build statistically reliable yet highly economical test collections.},
journal = {Inf. Retr.},
month = jun,
pages = {256–283},
numpages = {28}
}

@article{10.1007/s10791-015-9277-8,
author = {Goeuriot, Lorraine and Jones, Gareth J. and Kelly, Liadh and M\"{u}ller, Henning and Zobel, Justin},
title = {Medical Information Retrieval: Introduction to the Special Issue},
year = {2016},
issue_date = {April     2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9277-8},
doi = {10.1007/s10791-015-9277-8},
journal = {Inf. Retr.},
month = apr,
pages = {1–5},
numpages = {5}
}

@article{10.1007/s10791-015-9269-8,
author = {Palotti, Jo\~{a}o and Hanbury, Allan and M\"{u}ller, Henning and Kahn, Charles E.},
title = {How Users Search and What They Search for in the Medical Domain},
year = {2016},
issue_date = {April     2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9269-8},
doi = {10.1007/s10791-015-9269-8},
abstract = {The internet is an important source of medical knowledge for everyone, from laypeople to medical professionals. We investigate how these two extremes, in terms of user groups, have distinct needs and exhibit significantly different search behaviour. We make use of query logs in order to study various aspects of these two kinds of users. The logs from America Online, Health on the Net, Turning Research Into Practice and American Roentgen Ray Society (ARRS) GoldMiner were divided into three sets: (1) laypeople, (2) medical professionals (such as physicians or nurses) searching for health content and (3) users not seeking health advice. Several analyses are made focusing on discovering how users search and what they are most interested in. One possible outcome of our analysis is a classifier to infer user expertise, which was built. We show the results and analyse the feature set used to infer expertise. We conclude that medical experts are more persistent, interacting more with the search engine. Also, our study reveals that, conversely to what is stated in much of the literature, the main focus of users, both laypeople and professionals, is on disease rather than symptoms. The results of this article, especially through the classifier built, could be used to detect specific user groups and then adapt search results to the user group.},
journal = {Inf. Retr.},
month = apr,
pages = {189–224},
numpages = {36},
keywords = {User behavior, Query log analysis, Health search}
}

@article{10.1007/s10791-015-9268-9,
author = {Koopman, Bevan and Zuccon, Guido and Bruza, Peter and Sitbon, Laurianne and Lawley, Michael},
title = {Information Retrieval as Semantic Inference: A Graph Inference Model Applied to Medical Search},
year = {2016},
issue_date = {April     2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9268-9},
doi = {10.1007/s10791-015-9268-9},
abstract = {This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem--the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by keyword-based search methods, prompting us to perform additional relevance assessment on these new documents. A third of the newly retrieved documents judged were found to be relevant. Our analysis provides a thorough understanding of when and how to apply inference for retrieval, including a categorisation of queries according to the effect of inference. The inference mechanism promoted recall by retrieving new relevant documents not found by previous keyword-based approaches. In addition, it promoted precision by an effective reranking of documents. When inference is used, performance gains can generally be expected on hard queries. However, inference should not be applied universally: for easy, unambiguous queries and queries with few relevant documents, inference did adversely affect effectiveness. These conclusions reflect the fact that for retrieval as inference to be effective, a careful balancing act is involved. Finally, although the Graph Inference model is developed and applied to medical search, it is a general retrieval model applicable to other areas such as web search, where an emerging research trend is to utilise structured knowledge resources for more effective semantic search.},
journal = {Inf. Retr.},
month = apr,
pages = {6–37},
numpages = {32},
keywords = {Medical information retrieval, Semantic inference, Health informatics}
}

@article{10.1007/s10791-015-9265-z,
author = {Kovacs, William and Weisenthal, Samuel and Folio, Les and Li, Qiaoyi and Summers, Ronald M. and Yao, Jianhua},
title = {Retrieval, Visualization, and Mining of Large Radiation Dosage Data},
year = {2016},
issue_date = {April     2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9265-z},
doi = {10.1007/s10791-015-9265-z},
abstract = {Radiation dose monitoring has become an essential service that hospitals must perform. Depending on the system in place, this can result in the collection of large quantities of data, ripe for analysis. These data should include a wide variety of variables for each study because assessment of the propriety of the patient's dose is dependent on many factors, including patient age and size, as well as the body section that is being scanned. Moreover, the scanners themselves have many properties that affect patient dose, such as model, pitch and kVp. In this paper, we propose an engine that seamlessly integrated with a clinical PACS to retrieve radiation dosage data. We devised several schemes to analyze these data through visualization and mining techniques that examine it at different scopes. We demonstrate the utility of such visual methods at examining large, noisy, and multi-dimensional data, which is embodied in the collected radiation data.},
journal = {Inf. Retr.},
month = apr,
pages = {38–58},
numpages = {21},
keywords = {Radiation dose, Clinical PACS, Data retrieval, Data visualization}
}

@article{10.1007/s10791-015-9263-1,
author = {Zheng, Jiaping and Yu, Hong},
title = {Methods for Linking EHR Notes to Education Materials},
year = {2016},
issue_date = {April     2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9263-1},
doi = {10.1007/s10791-015-9263-1},
abstract = {It has been shown that accessing the patients' own electronic health records (EHR) can enhance their medical understanding and provide clinically relevant benefits. However, languages that are difficult for non-medical professionals to comprehend are prevalent in the EHR notes. The valuable and authoritative information contained in the EHR is thus less accessible to the patients, who ultimately stand to benefit the most from the information. To address this challenge, we are developing a system to retrieve EHR note-specific online consumer-oriented health education materials. We explored several query generation methods to convert long EHR notes to effective queries, including topic models and key concept identification. Our experiments show that queries using key concepts identified by a learning based model with pseudo-relevance feedback significantly outperform the baseline system of using the full text note.},
journal = {Inf. Retr.},
month = apr,
pages = {174–188},
numpages = {15},
keywords = {EHR note, Patient education, Information retrieval}
}

@article{10.1007/s10791-015-9262-2,
author = {Lossio-Ventura, Juan Antonio and Jonquet, Clement and Roche, Mathieu and Teisseire, Maguelonne},
title = {Biomedical Term Extraction: Overview and a New Methodology},
year = {2016},
issue_date = {April     2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9262-2},
doi = {10.1007/s10791-015-9262-2},
abstract = {Terminology
 extraction is an essential task in domain knowledge acquisition, as well as for information retrieval. It is also a mandatory first step aimed at building/enriching terminologies and ontologies. As often proposed in the literature, existing terminology extraction methods feature linguistic and statistical aspects and solve some problems related (but not completely) to term extraction, e.g. noise, silence, low frequency, large-corpora, complexity of the multi-word term extraction process. In contrast, we propose a cutting edge methodology to extract and to rank biomedical terms, covering all the mentioned problems. This methodology offers several measures based on linguistic, statistical, graphic and web aspects.
 These measures extract and rank candidate terms with excellent precision: we demonstrate that they outperform previously reported precision results for automatic term extraction, and work with different languages (English, French, and Spanish). We also demonstrate how the use of graphs and the web to assess the significance of a term candidate, enables us to outperform precision results. We evaluated our methodology on the biomedical GENIA and LabTestsOnline corpora and compared it with previously reported measures.},
journal = {Inf. Retr.},
month = apr,
pages = {59–99},
numpages = {41},
keywords = {Graphs, Automatic term extraction, Web mining, Natural language processing, Biomedical terminology extraction, BioNLP, Text mining}
}

@article{10.1007/s10791-015-9260-4,
author = {Markonis, Dimitrios and Schaer, Roger and M\"{u}ller, Henning},
title = {Evaluating Multimodal Relevance Feedback Techniques for Medical Image Retrieval},
year = {2016},
issue_date = {April     2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9260-4},
doi = {10.1007/s10791-015-9260-4},
abstract = {Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel relevance feedback technique that uses both text and visual information of the results is proposed. The two information modalities from the image examples are fused either at the feature level using the Rocchio algorithm or at the query list fusion step using a common late fusion rule. Results using the ImageCLEF 2012 benchmark database for medical image retrieval show the potential of relevance feedback techniques in medical image retrieval. The mean average precision (mAP) is used as the evaluation metric and the proposed method outperforms commonly-used methods. The baseline without feedback reached 16 % whereas the relevance feedback with 20 images reached up to 26.35 % with three steps and when using 100 images up to 34.87 % in four steps. Most improvements occur in the first two steps of relevance feedback and then results start to become relatively flat. This might also be due to only using positive feedback as negative feeback often also improves results after more steps. The effect of relevance feedback in automatically spelling corrected and translated queries is investigated as well. Results without mistakes were better than spell-corrected results but the spelling correction more than double results over non-corrected retrieval. Multimodal relevance feedback has shown to be able to help visual medical information retrieval. Next steps include integrating semantics into relevance feedback techniques to benefit from the structured knowledge of ontologies and experimenting on the fusion of text and visual information.},
journal = {Inf. Retr.},
month = apr,
pages = {100–112},
numpages = {13},
keywords = {Content-based image retrieval, Medical image retrieval, Relevance feedback}
}

@article{10.1007/s10791-015-9259-x,
author = {Roberts, Kirk and Simpson, Matthew and Demner-Fushman, Dina and Voorhees, Ellen and Hersh, William},
title = {State-of-the-Art in Biomedical Literature Retrieval for Clinical Cases: A Survey of the TREC 2014 CDS Track},
year = {2016},
issue_date = {April     2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9259-x},
doi = {10.1007/s10791-015-9259-x},
abstract = {Providing access to relevant biomedical literature in a clinical setting has the potential to bridge a critical gap in evidence-based medicine. Here, our goal is specifically to provide relevant articles to clinicians to improve their decision-making in diagnosing, treating, and testing patients. To this end, the TREC 2014 Clinical Decision Support Track evaluated a system's ability to retrieve relevant articles in one of three categories (Diagnosis, Treatment, Test) using an idealized form of a patient medical record
. Over 100 submissions from over 25 participants were evaluated on 30 topics, resulting in over 37k relevance judgments. In this article, we provide an overview of the task, a survey of the information retrieval methods employed by the participants, an analysis of the results, and a discussion on the future directions for this challenging yet important task.},
journal = {Inf. Retr.},
month = apr,
pages = {113–148},
numpages = {36},
keywords = {Clinical decision support, Information retrieval evaluation, Biomedical information retrieval}
}

@article{10.1007/s10791-015-9258-y,
author = {Soldaini, Luca and Yates, Andrew and Yom-Tov, Elad and Frieder, Ophir and Goharian, Nazli},
title = {Enhancing Web Search in the Medical Domain via Query Clarification},
year = {2016},
issue_date = {April     2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9258-y},
doi = {10.1007/s10791-015-9258-y},
abstract = {The majority of Internet users search for medical information online; however, many do not have an adequate medical vocabulary. Users might have difficulties finding the most authoritative and useful information because they are unfamiliar with the appropriate medical expressions describing their condition; consequently, they are unable to adequately satisfy their information need. We investigate the utility of bridging the gap between layperson and expert vocabularies; our approach adds the most appropriate expert expression to queries submitted by users, a task we call query clarification. We evaluated the impact of query clarification. Using three different synonym mappings and conducting two task-based retrieval studies, users were asked to answer medically-related questions using interleaved results from a major search engine. Our results show that the proposed system was preferred by users and helped them answer medical concerns correctly more often, with up to a 7 % increase in correct answers over an unmodified query. Finally, we introduce a supervised classifier to select the most appropriate synonym mapping for each query, which further increased the fraction of correct answers (12 %).},
journal = {Inf. Retr.},
month = apr,
pages = {149–173},
numpages = {25},
keywords = {Medical informatics, Health search, Query clarification, Personalized search}
}

@article{10.1007/s10791-015-9278-7,
author = {Yang, Peilin and Wang, Hongning and Fang, Hui and Cai, Deng},
title = {Opinions Matter: A General Approach to User Profile Modeling for Contextual Suggestion},
year = {2015},
issue_date = {December  2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9278-7},
doi = {10.1007/s10791-015-9278-7},
abstract = {
The increasing use of mobile devices enables an information retrieval (IR) system to capitalize on various types of contexts (e.g., temporal and geographical information) about its users. Combined with the user preference history recorded in the system, a better understanding of users' information need can be achieved and it thus leads to improved user satisfaction. More importantly, such a system could proactively recommend suggestions based on the contexts. User profiling is essential in contextual suggestion. However, given most users' observed behaviors are sparse and their preferences are latent in an IR system, constructing accurate user profiles is generally difficult. In this paper, we focus on location-based contextual suggestion and propose to leverage users' opinions to construct the profiles. Instead of simply recording "what places a user likes or dislikes" in the past (i.e., description-based profile), we want to construct a profile to identify "why a user likes or dislikes a place" so as to better predict whether the user would like a new candidate suggestion of place. By assuming users would like or dislike a place with similar reasons, we construct the opinion-based user profile in a collaborative way: opinions from the other users are leveraged to estimate a profile for the target user. Candidate suggestions are represented in the same fashion and ranked based on their similarities with respect to the user profiles. Moreover, we also develop a novel summary generation method that utilizes the opinion-based user profiles to generate personalized and high-quality summaries for the suggestions. Experiments are conducted over three standard TREC contextual suggestion collections and a Yelp data set. Extensive experiment comparisons confirm that the proposed opinion-based user modeling outperforms the existing description-based methods. In particular, the systems developed based on the proposed methods have been ranked as top 1 in both TREC 2013 and 2014 contextual suggestion tracks.
},
journal = {Inf. Retr.},
month = dec,
pages = {586–610},
numpages = {25},
keywords = {Opinions, Contextual suggestions, User modeling, Recommendation}
}

@article{10.1007/s10791-015-9272-0,
author = {Ganesan, Kavita and Zhai, Chengxiang},
title = {OpinoFetch: A Practical and Efficient Approach to Collecting Opinions on Arbitrary Entities},
year = {2015},
issue_date = {December  2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9272-0},
doi = {10.1007/s10791-015-9272-0},
abstract = {The abundance of opinions on the Web is now becoming a critical source of information in a variety of application areas such as business intelligence, market research and online shopping. Unfortunately, due to the rapid growth of online content, there is no one source to obtain a comprehensive set of opinions about a specific entity or a topic, making access to such content severely limited. While previous works have been focused on mining and summarizing online opinions, there is limited work on exploring the automatic collection of opinion content on the Web. In this paper, we propose a lightweight and practical approach to collecting opinion containing pages, namely review pages on the Web for arbitrary entities. We leverage existing Web search engines and use a novel information network called the FetchGraph to efficiently obtain review pages for entities of interest. Our experiments in three different domains show that our method is more effective than plain search engine results and we are able to collect entity specific review pages efficiently with reasonable precision and accuracy.
},
journal = {Inf. Retr.},
month = dec,
pages = {530–558},
numpages = {29},
keywords = {Opinion crawling, Review aggregation, Review crawling, Opinion analysis, Opinion aggregation, Opinion collection}
}

@article{10.1007/s10791-015-9271-1,
author = {Ren, Pengjie and Chen, Zhumin and Ma, Jun and Wang, Shuaiqiang and Zhang, Zhiwei and Ren, Zhaochun},
title = {Mining and Ranking Users' Intents behind Queries},
year = {2015},
issue_date = {December  2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9271-1},
doi = {10.1007/s10791-015-9271-1},
abstract = {
How to understand intents behind user queries is crucial towards improving the performance of Web search systems. NTCIR-11 IMine task focuses on this problem. In this paper, we address the NTCIR-11 IMine task with two phases referred to as Query Intent Mining (QIM) and Query Intent Ranking (QIR). (I) QIM is intended to mine users' potential intents by clustering short text fragments related to the given query. (II) QIR focuses on ranking those mined intents in a proper way. Two challenges exist in handling these tasks. (II) How to precisely estimate the intent similarity between user queries which only consist of a few words. (2) How to properly rank intents in terms of multiple factors, e.g. relevance, diversity, intent drift and so on. For the first challenge, we first investigate two interesting phenomena by analyzing query logs and document datasets, namely "Same-Intent-Co-Click" (SICC) and "Same-Intent-Similar-Rank" (SISR). SICC means that when users issue different queries, these queries represent the same intent if they click on the same URL. SISR means that if two queries denote the same intent, we should get similar search results when issuing them to a search engine. Then, we propose similarity functions for QIM based on the two phenomena. For the second challenge, we propose a novel intent ranking model which considers multiple factors as a whole. We perform extensive experiments and an interesting case study on the Chinese dataset of NTCIR-11 IMine task. Experimental results demonstrate the effectiveness of our proposed approaches in terms of both QIM and QIR.
},
journal = {Inf. Retr.},
month = dec,
pages = {504–529},
numpages = {26},
keywords = {Intent mining, Information retrieval, Query understanding, NTCIR-11 IMine task, Intent ranking}
}

@article{10.1007/s10791-015-9270-2,
author = {Giachanou, Anastasia and Salampasis, Michail and Paltoglou, Georgios},
title = {Multilayer Source Selection as a Tool for Supporting Patent Search and Classification},
year = {2015},
issue_date = {December  2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9270-2},
doi = {10.1007/s10791-015-9270-2},
abstract = {In this paper we present a method that can be used to attain specific objectives in a typical prior art search process. The objectives are first to assist patent searchers in understanding the underlying technical concepts of a patent by identifying relevant international patent classification (IPC) codes and second to help them conduct a filtered search based on automatically selected IPCs. We view the automated selection of IPCs as a collection selection problem from the domain of distributed information retrieval (DIR) that can be addressed using existing DIR methods, which we extend and adapt for the patent domain. Our work exploits the intellectually assigned classifications codes that are used to categorize patents and to facilitate patent searches. In our method, manually assigned IPC codes of patent documents are used to cluster, distribute and index patents through hundreds or thousands of sub-collections. We propose a new multilayer collection selection method that effectively suggests classification codes exploiting the hierarchical classification schemes such as IPC/CPC. The new method in addition to utilizing the topical relevance of IPCs at a particular level of interest exploits the topical relevance of their ancestors in the IPC hierarchy and aggregates those multiple estimations of relevance to a single estimation. Experimental results on the CLEF-IP 2011 dataset show that the proposed approach outperforms state-of-art methods from the DIR domain not only in identifying relevant IPC codes but also in retrieving relevant patent documents given a patent query.},
journal = {Inf. Retr.},
month = dec,
pages = {559–585},
numpages = {27},
keywords = {Patent classification, IPC suggestion, Distributed information retrieval, IPC, Patent search}
}

@article{10.1007/s10791-015-9267-x,
author = {Liu, Xitong and Fang, Hui},
title = {Latent Entity Space: A Novel Retrieval Approach for Entity-Bearing Queries},
year = {2015},
issue_date = {December  2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9267-x},
doi = {10.1007/s10791-015-9267-x},
abstract = {Analysis on Web search query logs has revealed that there is a large portion of entity-bearing queries, reflecting the increasing demand of users on retrieving relevant information about entities such as persons, organizations, products, etc. In the meantime, significant progress has been made in Web-scale information extraction, which enables efficient entity extraction from free text. Since an entity is expected to capture the semantic content of documents and queries more accurately than a term, it would be interesting to study whether leveraging the information about entities can improve the retrieval accuracy for entity-bearing queries. In this paper, we propose a novel retrieval approach, i.e., latent entity space (LES), which models the relevance by leveraging entity profiles to represent semantic content of documents and queries. In the LES, each entity corresponds to one dimension, representing one semantic relevance aspect. We propose a formal probabilistic framework to model the relevance in the high-dimensional entity space. Experimental results over TREC collections show that the proposed LES approach is effective in capturing latent semantic content and can significantly improve the search accuracy of several state-of-the-art retrieval models for entity-bearing queries.},
journal = {Inf. Retr.},
month = dec,
pages = {473–503},
numpages = {31},
keywords = {Entity profile, Document retrieval, Latent entity space}
}

@article{10.1007/s10791-015-9266-y,
author = {Tonon, Alberto and Demartini, Gianluca and Cudr\'{e}-Mauroux, Philippe},
title = {Pooling-Based Continuous Evaluation of Information Retrieval Systems},
year = {2015},
issue_date = {October   2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9266-y},
doi = {10.1007/s10791-015-9266-y},
abstract = {The dominant approach to evaluate the effectiveness of information retrieval (IR) systems is by means of reusable test collections built following the Cranfield paradigm. In this paper, we propose a new IR evaluation methodology based on pooled test-collections and on the continuous use of either crowdsourcing or professional editors to obtain relevance judgements. Instead of building a static collection for a finite set of systems known a priori, we propose an IR evaluation paradigm where retrieval approaches are evaluated iteratively on the same collection. Each new retrieval technique takes care of obtaining its missing relevance judgements and hence contributes to augmenting the overall set of relevance judgements of the collection. We also propose two metrics: Fairness Score, and opportunistic number of relevant documents, which we then use to define new pooling strategies. The goal of this work is to study the behavior of standard IR metrics, IR system ranking, and of several pooling techniques in a continuous evaluation context by comparing continuous and non-continuous evaluation results on classic test collections. We both use standard and crowdsourced relevance judgements, and we actually run a continuous evaluation campaign over several existing IR systems.},
journal = {Inf. Retr.},
month = oct,
pages = {445–472},
numpages = {28},
keywords = {Continuous evaluation, Pooling techniques, Crowdsourcing, Information retrieval evaluation}
}

@article{10.1007/s10791-015-9264-0,
author = {Mao, Jin and Lu, Kun and Mu, Xiangming and Li, Gang},
title = {Mining Document, Concept, and Term Associations for Effective Biomedical Retrieval: Introducing MeSH-Enhanced Retrieval Models},
year = {2015},
issue_date = {October   2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9264-0},
doi = {10.1007/s10791-015-9264-0},
abstract = {Manually assigned subject terms, such as Medical Subject Headings (MeSH) in the health domain, describe the concepts or topics of a document. Existing information retrieval models do not take full advantage of such information. In this paper, we propose two MeSH-enhanced (ME) retrieval models that integrate the concept layer (i.e. MeSH) into the language modeling framework to improve retrieval performance. The new models quantify associations between documents and their assigned concepts to construct conceptual representations for the documents, and mine associations between concepts and terms to construct generative concept models. The two ME models reconstruct two essential estimation processes of the relevance model (Lavrenko and Croft 2001) by incorporating the document-concept and the concept-term associations. More specifically, in Model 1, language models of the pseudo-feedback documents are enriched by their assigned concepts. In Model 2, concepts that are related to users' queries are first identified, and then used to reweight the pseudo-feedback documents according to the document-concept associations. Experiments carried out on two standard test collections show that the ME models outperformed the query likelihood model, the relevance model (RM3), and an earlier ME model. A detailed case analysis provides insight into how and why the new models improve/worsen retrieval performance. Implications and limitations of the study are discussed. This study provides new ways to formally incorporate semantic annotations, such as subject terms, into retrieval models. The findings of this study suggest that integrating the concept layer into retrieval models can further improve the performance over the current state-of-the-art models.},
journal = {Inf. Retr.},
month = oct,
pages = {413–444},
numpages = {32},
keywords = {MeSH-enhanced retrieval models, Relevance model, Concept, Health information retrieval}
}

@article{10.1007/s10791-015-9261-3,
author = {Qian, Yanan and Zheng, Qinghua and Sakai, Tetsuya and Ye, Junting and Liu, Jun},
title = {Dynamic Author Name Disambiguation for Growing Digital Libraries},
year = {2015},
issue_date = {October   2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9261-3},
doi = {10.1007/s10791-015-9261-3},
abstract = {When a digital library user searches for publications by an author name, she often sees a mixture of publications by different authors who have the same name. With the growth of digital libraries and involvement of more authors,
this author ambiguity problem is becoming critical. Author disambiguation (AD) often tries to solve this problem by leveraging metadata such as coauthors, research topics, publication venues and citation information, since more personal information such as the contact details is often restricted or missing. In this paper, we study the problem of how to efficiently disambiguate author names given an incessant stream of published papers. To this end, we propose a "BatchAD+IncAD" framework for dynamic author disambiguation. First, we perform batch author disambiguation (BatchAD) to disambiguate all author names at a given time by grouping all records (each record refers to a paper with one of its author names) into disjoint clusters. This establishes a one-to-one mapping between the clusters and real-world authors. Then, for newly added papers, we periodically perform incremental author disambiguation (IncAD), which determines whether each new record can be assigned to an existing cluster, or to a new cluster not yet included in the previous data. Based on the new data, IncAD also tries to correct previous AD results. Our main contributions are: (1) We demonstrate with real data that a small number of new papers often have overlapping author names with a large portion of existing papers, so it is challenging for IncAD to effectively leverage previous AD results. (2) We propose a novel IncAD model which aggregates metadata from a cluster of records to estimate the author's profile such as her coauthor distributions and keyword distributions, in order to predict how likely it is that a new record is "produced" by the author. (3) Using two labeled datasets and one large-scale raw dataset, we show that the proposed method is much more efficient than state-of-the-art methods while ensuring high accuracy.},
journal = {Inf. Retr.},
month = oct,
pages = {379–412},
numpages = {34},
keywords = {Multi-classification, Data stream, Digital library, Clustering, Author disambiguation}
}

@article{10.1007/s10791-015-9257-z,
author = {Lv, Yuanhua and Zhai, Chengxiang},
title = {Negative Query Generation: Bridging the Gap between Query Likelihood Retrieval Models and Relevance},
year = {2015},
issue_date = {August    2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9257-z},
doi = {10.1007/s10791-015-9257-z},
abstract = {The language modeling approach to information retrieval has recently attracted much attention. In the language modeling retrieval models, we can score and rank documents based on the query likelihood method. From the theoretical perspective, however, the justification of the existing (standard) query likelihood method based on the probability ranking principle requires an unrealistic assumption about the generation of a "negative query" from a document, which states that the probability that a user who dislikes a document would use a query does not depend on the particular document. This assumption enables ignoring the negative query generation so as to justify using the basic query likelihood method as a retrieval function. In reality, however, this assumption does not hold because a user who dislikes a document would more likely avoid using words in the document when posing a query. This suggests that the standard query likelihood function is a potentially non-optimal retrieval function. In this paper, we attempt to improve the standard language modeling retrieval models by bringing back the component of negative query generation. Specifically, we propose a general and efficient approach to estimate document-dependent probabilities of negative query generation based on the principle of maximum entropy, and derive a more complete query likelihood retrieval function that also contains the negative query generation component. In addition, we further develop a more general probabilistic distance retrieval method to naturally incorporate query language models, which covers the proposed query likelihood with negative query generation as its special case. The proposed approaches not only bridge the theoretic gap between the standard language modeling retrieval models and the notion of relevance, but also improves the retrieval effectiveness with (almost) no additional computational cost.
},
journal = {Inf. Retr.},
month = aug,
pages = {359–378},
numpages = {20},
keywords = {Relevance, Query likelihood, Principle of maximum entropy, Probability ranking principle, Language model, Negative query generation}
}

@article{10.1007/s10791-015-9256-0,
author = {Arampatzis, Avi and Drosatos, George and Efraimidis, Pavlos S.},
title = {Versatile Query Scrambling for Private Web Search},
year = {2015},
issue_date = {August    2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9256-0},
doi = {10.1007/s10791-015-9256-0},
abstract = {We
consider the problem of privacy leaks suffered by Internet users when they perform web searches, and propose a framework to mitigate them. In brief, given a `sensitive' search query, the objective of our work is to retrieve the target documents from a search engine without disclosing the actual query. Our approach, which builds upon and improves recent work on search privacy, approximates the target search results by replacing the private user query with a set of blurred or scrambled queries. The results of the scrambled queries are then used to cover the private user interest. We model the problem theoretically, define a set of privacy objectives with respect to web search and investigate the effectiveness of the proposed solution with a set of queries with privacy issues on a large web collection. Experiments show great improvements in retrieval effectiveness over a previously reported baseline in the literature. Furthermore, the methods are more versatile, predictably-behaved, applicable to a wider range of information needs, and the privacy they provide is more comprehensible to the end-user. Additionally, we investigate the perceived privacy via a user study, as well as, measure the system's usefulness taking into account the trade off between retrieval effectiveness and privacy. The practical feasibility of the methods is demonstrated in a field experiment, scrambling queries against a popular web search engine. The findings may have implications for other IR research areas, such as query expansion, query decomposition, and distributed retrieval.},
journal = {Inf. Retr.},
month = aug,
pages = {331–358},
numpages = {28},
keywords = {Inter-user agreement, Set covering, Query-based document sampling, Search privacy, Query scrambler, Mutual information}
}

@article{10.1007/s10791-015-9254-2,
author = {Jameel, Shoaib and Lam, Wai and Bing, Lidong},
title = {Supervised Topic Models with Word Order Structure for Document Classification and Retrieval Learning},
year = {2015},
issue_date = {August    2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9254-2},
doi = {10.1007/s10791-015-9254-2},
abstract = {One limitation of most existing probabilistic latent topic models for document classification is that the topic model itself does not consider useful side-information, namely, class labels of documents. Topic models, which in turn consider the side-information, popularly known as supervised topic models, do not consider the word order structure in documents. One of the motivations behind considering the word order structure is to capture the semantic fabric of the document. We investigate a low-dimensional latent topic model for document classification. Class label information and word order structure are integrated into a supervised topic model enabling a more effective interaction among such information for solving document classification. We derive a collapsed Gibbs sampler for our model. Likewise, supervised topic models with word order structure have not been explored in document retrieval learning. We propose a novel supervised topic model for document retrieval learning which can be regarded as a pointwise model for tackling the learning-to-rank task. Available relevance assessments and word order structure are integrated into the topic model itself.
 We conduct extensive experiments on several publicly available benchmark datasets, and show that our model improves upon the state-of-the-art models.},
journal = {Inf. Retr.},
month = aug,
pages = {283–330},
numpages = {48},
keywords = {Learning-to-rank, Structured topic model, Document classification, Maximum-margin, Topic modeling}
}

@article{10.1007/s10791-015-9255-1,
author = {Rahimi, Razieh and Shakery, Azadeh and King, Irwin},
title = {Multilingual Information Retrieval in the Language Modeling Framework},
year = {2015},
issue_date = {June      2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9255-1},
doi = {10.1007/s10791-015-9255-1},
abstract = {Multilingual information retrieval (MLIR) provides results that are more comprehensive than those of mono- and cross-lingual retrieval. Methods for MLIR are categorized as: (1) Fusion-based methods that merge results from multiple retrieval runs, and (2) Direct methods that build a unique index for the entire collection. Merging results of individual runs reduces the overall effectiveness, while more effective direct methods suffer from either time complexity and memory overhead, or over-weighting of index terms. In this paper, we propose a direct MLIR approach by using the language modeling framework that includes a novel multilingual language model estimation for documents, and a new way to globally estimate word statistics. These contributions enable ranking documents in multiple languages in one retrieval phase without having the problems of the previous direct methods. Moreover, our approach has the advantage of accommodating multilingual feedback information which helps to prevent query drift, and consequently to improve the performance. Finally, we effectively address the common case of incomplete coverage of translation resources in our proposed estimation methods. Experimental results show that the proposed approach outperforms the previous MLIR approaches.},
journal = {Inf. Retr.},
month = jun,
pages = {246–281},
numpages = {36},
keywords = {Multilingual language models, Multilingual feedback, Language modeling framework, KL-divergence framework, Multilingual information retrieval}
}

@article{10.1007/s10791-015-9253-3,
author = {Niu, Shuzi and Lan, Yanyan and Guo, Jiafeng and Wan, Shengxian and Cheng, Xueqi},
title = {Which Noise Affects Algorithm Robustness for Learning to Rank},
year = {2015},
issue_date = {June      2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9253-3},
doi = {10.1007/s10791-015-9253-3},
abstract = {When applying learning to rank algorithms in real search applications, noise in human labeled training data becomes an inevitable problem which will affect the performance of the algorithms. Previous work mainly focused on studying how noise affects ranking algorithms and how to design robust ranking algorithms. In our work, we investigate what inherent characteristics make training data robust to label noise and how to utilize them to guide labeling. The motivation of our work comes from an interesting observation that a same ranking algorithm may show very different sensitivities to label noise over different data sets. We thus investigate the underlying reason for this observation based on three typical kinds of learning to rank algorithms (i.e. pointwise, pairwise and listwise methods) and three public data sets (i.e. OHSUMED, TD2003 and MSLR-WEB10K) with different properties. We find that when label noise increases in training data, it is the document pair noise ratio (referred to as pNoise) rather than document noise ratio (referred to as dNoise) that can well explain the performance degradation of a ranking algorithm. We further identify two inherent characteristics of the training data, namely relevance levels and label balance, that have great impact on the variation of pNoise with respect to label noise (i.e. dNoise). According to these above results, we further discuss some guidelines on the labeling strategy to construct robust training data for learning to rank algorithms in practice.},
journal = {Inf. Retr.},
month = jun,
pages = {215–245},
numpages = {31},
keywords = {Learning to rank, Label noise, Robust data}
}

@article{10.1007/s10791-015-9252-4,
author = {Forsati, Rana and Moayedikia, Alireza and Shamsfard, Mehrnoush},
title = {An Effective Web Page Recommender Using Binary Data Clustering},
year = {2015},
issue_date = {June      2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9252-4},
doi = {10.1007/s10791-015-9252-4},
abstract = {Through growth of the Web, the amount of data on the net is growing in an uncontrolled way, that makes it hard for the users to find the relevant and required information- an issue which is usually referred to as information overload. Recommender systems are among the appealing methods that can handle this problem effectively. Theses systems are either based on collaborative filtering and content based approaches, or rely on rating of items and the behavior of the users to generate customized recommendations. In this paper we propose an efficient Web page recommender by exploiting session data of users. To this end, we propose a novel clustering algorithm to partition the binary session data into a fixed number of clusters and utilize the partitioned sessions to make recommendations. The proposed binary clustering algorithm is scalable and employs a novel method to find the representative of a set of binary vectors to represent the center of clusters--that might be interesting in its own right. In addition, the proposed clustering algorithm is integrated with the $$k$$k-means algorithm to achieve better clustering quality by combining its explorative power with fine-tuning power of the $$k$$k-means algorithm. We have performed extensive experiments on a real dataset to demonstrate the advantages of proposed binary data clustering methods and Web page recommendation algorithm. In particular, the proposed recommender system is compared to previously published works in terms of minimum frequency and based on the number of recommended pages to show its superiority in terms of accuracy, coverage and F-measure.},
journal = {Inf. Retr.},
month = jun,
pages = {167–214},
numpages = {48},
keywords = {Recommender systems, k-Means, Binary data clustering, Harmony search optimization}
}

@article{10.1007/s10791-015-9251-5,
author = {Sloan, Marc and Yang, Hui and Wang, Jun},
title = {A Term-Based Methodology for Query Reformulation Understanding},
year = {2015},
issue_date = {April     2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9251-5},
doi = {10.1007/s10791-015-9251-5},
abstract = {Key to any research involving session search is the understanding of how a user's queries evolve throughout the session. When a user creates a query reformulation, he or she is consciously retaining terms from their original query, removing others and adding new terms. By measuring the similarity between queries we can make inferences on the user's information need and how successful their new query is likely to be. By identifying the origins of added terms we can infer the user's motivations and gain an understanding of their interactions. In this paper we present a novel term-based methodology for understanding and interpreting query reformulation actions. We use TREC Session Track data to demonstrate how our technique is able to learn from query logs and we make use of click data to test user interaction behavior when reformulating queries. We identify and evaluate a range of term-based query reformulation strategies and show that our methods provide valuable insight into understanding query reformulation in session search.
},
journal = {Inf. Retr.},
month = apr,
pages = {145–165},
numpages = {21},
keywords = {Query reformulation, Click model, Term model}
}

@article{10.1007/s10791-015-9250-6,
author = {Cai, Wenbin and Zhang, Muhan and Zhang, Ya},
title = {Active Learning for Ranking with Sample Density},
year = {2015},
issue_date = {April     2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9250-6},
doi = {10.1007/s10791-015-9250-6},
abstract = {While ranking is widely used in many online domains such as search engines and recommendation systems, it is non-trivial to label enough data examples to build a high performance machine-learned ranking model. To relieve this problem, active learning has been proposed, which selectively labels the most informative examples. However, data density, which has been proven helpful for data sampling in general, is ignored by most of the existing active learning for ranking studies. In this paper, we propose a novel active learning for ranking framework, generalization error minimization (GEM), which incorporates data density in minimizing generalization error. Concentrating on active learning for search ranking, we employ classical kernel density estimation to infer data density. Considering the unique query---document structure in ranking data, we estimate sample density at both query level and document level. Under the GEM framework, we propose new active learning algorithms at both query level and document level. Experimental results on the LETOR 4.0 data set and a real-world Web search ranking data set from a commercial search engine have demonstrated the effectiveness of the proposed active learning algorithms.},
journal = {Inf. Retr.},
month = apr,
pages = {123–144},
numpages = {22},
keywords = {Density, Learning to rank, Active learning}
}

@article{10.1007/s10791-014-9249-4,
author = {Lacerda, Anisio and Santos, Rodrygo L. and Veloso, Adriano and Ziviani, Nivio},
title = {Improving Daily Deals Recommendation Using Explore-Then-Exploit Strategies},
year = {2015},
issue_date = {April     2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9249-4},
doi = {10.1007/s10791-014-9249-4},
abstract = {Daily-Deals Sites (DDSs) enable local businesses, such as restaurants and stores, to promote their products and services and to increase their sales by offering customers significantly reduced prices. If a customer finds a relevant deal in the catalog of electronic coupons, she can purchase it and the DDS receives a commission. Thus, offering relevant deals to customers maximizes the profitability of the DDS. An immediate strategy, therefore, would be to apply existing recommendation algorithms to suggest deals that are potentially relevant to specific customers, enabling more appealing, effective and personalized catalogs. However, this strategy may be innocuous because (1) most of the customers are sporadic bargain hunters, and thus past preference data is extremely sparse, (2) deals have a short living period, and thus data is extremely volatile, and (3) customers' taste and interest may undergo temporal drifts. In order to address such a particularly challenging scenario, we propose a new algorithm for daily deals recommendation based on an explore-then-exploit strategy. Basically, we choose a fraction of the customers to gather feedback on the current catalog in the exploration phase, and the remaining customers to receive improved recommendations based on the previously gathered feedback in a posterior exploitation phase. During exploration, a co-purchase network structure is updated with customer feedback (i.e., the purchases of the day), and during exploitation the updated network is used to enrich the recommendation algorithm. An advantage of our approach is that it is agnostic to the underlying recommender algorithm. Using real data obtained from a large DDS in Brazil, we show that the way in which we split customers into exploration and exploitation impacts by large the effectiveness of the recommendations. We evaluate different splitting strategies based on network centrality metrics and show that our approach offers gains in mean average precision and mean reciprocal rank ranging from 14 to 34 % when applied on top of state-of-the-art recommendation algorithms.},
journal = {Inf. Retr.},
month = apr,
pages = {95–122},
numpages = {28},
keywords = {Recommender systems, Armed bandit setting, Daily-deals sites}
}

@article{10.1007/s10791-014-9248-5,
author = {Santos, A\'{e}cio S. and Carvalho, Cristiano R. and Almeida, Jussara M. and Moura, Edleno S. and Silva, Altigran S. and Ziviani, Nivio},
title = {A Genetic Programming Framework to Schedule Webpage Updates},
year = {2015},
issue_date = {February  2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9248-5},
doi = {10.1007/s10791-014-9248-5},
abstract = {The quality of a Web search engine is influenced by several factors, including coverage and the freshness of the content gathered by the web crawler. Focusing particularly on freshness, one key challenge is to estimate the likelihood of a previously crawled webpage being modified.
Such estimates are used to define the order in which those pages should be visited, and thus, can be exploited to reduce the cost of monitoring crawled webpages for keeping updated versions. We here present a Genetic Programming framework, called $$ GP4C $$GP4C--Genetic Programming for Crawling, to generate score functions that produce accurate rankings of pages regarding their probabilities of having been modified. We compare $$ GP4C $$GP4C with state-of-the-art methods using a large dataset of webpages crawled from the Brazilian Web. Our evaluation includes multiple performance metrics and several variations of our framework, built from exploring different sets of terminals and fitness functions. In particular, we evaluate $$ GP4C $$GP4C using the ChangeRate and Normalized Discounted Cumulative Gain (NDCG) metrics as both objective function and evaluation metric. We show that, in comparison with ChangeRate, NDCG has the ability of better evaluating the effectiveness of scheduling strategies, since it is able to take the ranking produced by the scheduling into account.},
journal = {Inf. Retr.},
month = feb,
pages = {73–94},
numpages = {22},
keywords = {Scheduling functions, Web crawling, Genetic Programming}
}

@article{10.1007/s10791-014-9247-6,
author = {Dohnal, Vlastislav and Homola, Tomas and Zezula, Pavel},
title = {MDPV: Metric Distance Permutation Vocabulary},
year = {2015},
issue_date = {February  2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9247-6},
doi = {10.1007/s10791-014-9247-6},
abstract = {Sub-image content-based similarity search forms an important operation in current image archives since it provides users with images that contain a query image as their part. Such a search can conveniently be implemented using the bag-of-features model. Its integral part is a construction of visual vocabulary. Most existing algorithms to create a visual vocabulary suffer from high computational (e.g. k-means) or supervisor-guidance (e.g. visual-bit classifier, or sparse coding) requirements. In this paper, we propose a novel approach to visual vocabulary construction called metric distance permutation vocabulary. It is based on permutations of metric distances to create compact visual words. Its major advantage over prior techniques is time and space efficiency of vocabulary construction and quantization process during querying, while achieving comparable or even better effectiveness (query result quality). Moreover, this basic concept is extended to combine more independent permutations. Both the proposals are experimented on well-known real-world data-sets and compared to other state-of-the-art techniques.},
journal = {Inf. Retr.},
month = feb,
pages = {51–72},
numpages = {22},
keywords = {Feature quantization, Metric distance permutation vocabulary, Visual vocabulary, k-Means clustering, Bag-of-features model}
}

@article{10.1007/s10791-014-9246-7,
author = {Wu, Haocheng and Hu, Yunhua and Li, Hang and Chen, Enhong},
title = {A New Approach to Query Segmentation for Relevance Ranking in Web Search},
year = {2015},
issue_date = {February  2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9246-7},
doi = {10.1007/s10791-014-9246-7},
abstract = {In this paper, we try to determine how best to improve state-of-the-art methods for relevance ranking in web searching by query segmentation. Query segmentation is meant to separate the input query into segments, typically natural language phrases. We propose employing the re-ranking approach in query segmentation, which first employs a generative model to create the top k candidates and then employs a discriminative model to re-rank the candidates to obtain the final segmentation result. The method has been widely utilized for structure prediction in natural language processing, but has not been applied to query segmentation, as far as we know. Furthermore, we propose a new method for using the results of query segmentation in relevance ranking, which takes both the original query words and the segmented query phrases as units of query representation. We investigate whether our method can improve three relevance models, namely n-gram BM25, key n-gram model and term dependency model, within the framework of learning to rank. Our experimental results on large scale web search datasets show that our method can indeed significantly improve relevance ranking in all three cases.},
journal = {Inf. Retr.},
month = feb,
pages = {26–50},
numpages = {25},
keywords = {Web search, Re-ranking, BM25, Query processing, Term dependency model, Query segmentation, Key n-gram extraction, Relevance ranking}
}

@article{10.1007/s10791-014-9244-9,
author = {Li, Ximing and Ouyang, Jihong and Lu, You and Zhou, Xiaotang and Tian, Tian},
title = {Group Topic Model: Organizing Topics into Groups},
year = {2015},
issue_date = {February  2015},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {18},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9244-9},
doi = {10.1007/s10791-014-9244-9},
abstract = {
Latent Dirichlet allocation defines hidden topics to capture latent semantics in text documents. However, it assumes that all the documents are represented by the same topics, resulting in the "forced topic" problem. To solve this problem, we developed a group latent Dirichlet allocation (GLDA). GLDA uses two kinds of topics: local topics and global topics. The highly related local topics are organized into groups to describe the local semantics, whereas the global topics are shared by all the documents to describe the background semantics. GLDA uses variational inference algorithms for both offline and online data. We evaluated the proposed model for topic modeling and document clustering. Our experimental results indicated that GLDA can achieve a competitive performance when compared with state-of-the-art approaches.
},
journal = {Inf. Retr.},
month = feb,
pages = {1–25},
numpages = {25},
keywords = {Document clustering, Topic modeling, Online learning, Group, Latent Dirichlet allocation, Variational inference}
}

@article{10.1007/s10791-014-9245-8,
author = {Hanbury, Allan and Lupu, Mihai and Kando, Noriko and Diallo, Barrou and Adams, Stephen},
title = {Guest Editorial: Special Issue on Information Retrieval in the Intellectual Property Domain},
year = {2014},
issue_date = {October   2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {5–6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9245-8},
doi = {10.1007/s10791-014-9245-8},
journal = {Inf. Retr.},
month = oct,
pages = {407–411},
numpages = {5}
}

@article{10.1007/s10791-014-9239-6,
author = {D'hondt, Eva and Verberne, Suzan and Oostdijk, Nelleke and Beney, Jean and Koster, Cornelius and Boves, Lou},
title = {Dealing with Temporal Variation in Patent Categorization},
year = {2014},
issue_date = {October   2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {5–6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9239-6},
doi = {10.1007/s10791-014-9239-6},
abstract = {
In this paper, we quantify the existence of concept drift in patent data, and examine its impact on classification accuracy. When developing algorithms for classifying incoming patent applications with respect to their category in the International Patent Classification (IPC) hierarchy, a temporal mismatch between training data and incoming documents may deteriorate classification results. We measure the effect of this temporal mismatch and aim to tackle it by optimal selection of training data. To illustrate the various aspects of concept drift on IPC class level, we first perform quantitative analyses on a subset of English abstracts extracted from patent documents in the CLEF-IP 2011 patent corpus. In a series of classification experiments, we then show the impact of temporal variation on the classification accuracy of incoming applications. We further examine what training data selection method, combined with our classification approach yields the best classifier; and how combining different text representations may improve patent classification. We found that using the most recent data is a better strategy than static sampling but that extending a set of recent training data with older documents does not harm classification performance. In addition, we confirm previous findings that using 2-skip-2-grams on top of the bag of unigrams structurally improves patent classification. Our work is an important contribution to the research into concept drift for text classification, and to the practice of classifying incoming patent applications.},
journal = {Inf. Retr.},
month = oct,
pages = {520–544},
numpages = {25},
keywords = {Concept drift, Text representation, Patent classification}
}

@article{10.1007/s10791-014-9238-7,
author = {Tannebaum, Wolfgang and Rauber, Andreas},
title = {Using Query Logs of USPTO Patent Examiners for Automatic Query Expansion in Patent Searching},
year = {2014},
issue_date = {October   2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {5–6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9238-7},
doi = {10.1007/s10791-014-9238-7},
abstract = {In the patent domain significant efforts are invested to assist researchers in formulating better queries, preferably via automated query expansion. Currently, automatic query expansion in patent search is mostly limited to computing co-occurring terms for the searchable features of the invention. Additional query terms are extracted automatically from patent documents based on entropy measures. Learning synonyms in the patent domain for automatic query expansion has been a difficult task. No dedicated sources providing synonyms for the patent domain, such as patent domain specific lexica or thesauri, are available. In this paper we focus on the highly professional search setting of patent examiners. In particular, we use query logs to learn synonyms for the patent domain. For automatic query expansion, we create term networks based on the query logs specifically for several USPTO patent classes. Experiments show good performance in automatic query expansion using these automatically generated term networks. Specifically, with a larger number of query logs for a specific patent US class available the performance of the learned term networks increases.},
journal = {Inf. Retr.},
month = oct,
pages = {452–470},
numpages = {19},
keywords = {Patent searching, Query expansion, Query log analysis}
}

@article{10.1007/s10791-013-9236-1,
author = {Zhou, Dong and Truran, Mark and Liu, Jianxun and Zhang, Sanrong},
title = {Using Multiple Query Representations in Patent Prior-Art Search},
year = {2014},
issue_date = {October   2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {5–6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9236-1},
doi = {10.1007/s10791-013-9236-1},
abstract = {
Before a patent application is made, it is important to search the appropriate databases for prior-art (i.e., pre-existing patents that may affect the validity of the application). Previous work on prior-art search has concentrated on single query representations of the patent application. In the following paper, we describe an approach which uses multiple query representations. We evaluate our technique using a well-known test collection (CLEF-IP 2011). Our results suggest that multiple query representations significantly outperform single query representations.},
journal = {Inf. Retr.},
month = oct,
pages = {471–491},
numpages = {21},
keywords = {Prior-art, Patent search, Collaborative filtering}
}

@article{10.1007/s10791-013-9234-3,
author = {Rusi\~{n}ol, Mar\c{c}al and Heras, Llu\'{\i}s-Pere and Terrades, Oriol Ramos},
title = {Flowchart Recognition for Non-Textual Information Retrieval in Patent Search},
year = {2014},
issue_date = {October   2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {5–6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9234-3},
doi = {10.1007/s10791-013-9234-3},
abstract = {Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset.},
journal = {Inf. Retr.},
month = oct,
pages = {545–562},
numpages = {18},
keywords = {Flowchart recognition, Raster-to-vector conversion, Patent documents, Text/graphics separation, Symbol recognition}
}

@article{10.1007/s10791-013-9233-4,
author = {Al-Shboul, Bashar and Myaeng, Sung-Hyon},
title = {Wikipedia-Based Query Phrase Expansion in Patent Class Search},
year = {2014},
issue_date = {October   2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {5–6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9233-4},
doi = {10.1007/s10791-013-9233-4},
abstract = {Relevance feedback methods generally suffer from topic drift caused by word ambiguities and synonymous uses of words. Topic drift is an important issue in patent information retrieval as people tend to use different expressions describing similar concepts causing low precision and recall at the same time. Furthermore, failing to retrieve relevant patents to an application during the examination process may cause legal problems caused by granting an existing invention. A possible cause of topic drift is utilizing a relevance feedback-based search method. As a way to alleviate the inherent problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with phrases disambiguating the original query words. The idea was implemented for patent search where patents are classified into a hierarchy of categories, and the analyses of the experimental results showed not only the positive roles of phrases and words in retrieving additional relevant documents through query expansion but also their contributions to alleviating the query drift problem. More specifically, our query expansion method was compared against relevance-based language model, a state-of-the-art query expansion method, to show its superiority in terms of MAP on all levels of the classification hierarchy.},
journal = {Inf. Retr.},
month = oct,
pages = {430–451},
numpages = {22},
keywords = {Phrase-based query expansion, Patent search, Clarity, Retrievability, Wikipedia categories}
}

@article{10.1007/s10791-013-9232-5,
author = {Mahdabi, Parvaz and Crestani, Fabio},
title = {The Effect of Citation Analysis on Query Expansion for Patent Retrieval},
year = {2014},
issue_date = {October   2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {5–6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9232-5},
doi = {10.1007/s10791-013-9232-5},
abstract = {Patent prior art search is a type of search in the patent domain where documents are searched for that describe the work previously carried out related to a patent application. The goal of this search is to check whether the idea in the patent application is novel. Vocabulary mismatch is one of the main problems of patent retrieval which results in low retrievability of similar documents for a given patent application. In this paper we show how the term distribution of the cited documents in an initially retrieved ranked list can be used to address the vocabulary mismatch. We propose a method for query modeling estimation which utilizes the citation links in a pseudo relevance feedback set. We first build a topic dependent citation graph, starting from the initially retrieved set of feedback documents and utilizing citation links of feedback documents to expand the set. We identify the important documents in the topic dependent citation graph using a citation analysis measure. We then use the term distribution of the documents in the citation graph to estimate a query model by identifying the distinguishing terms and their respective weights. We then use these terms to expand our original query. We use CLEF-IP 2011 collection to evaluate the effectiveness of our query modeling approach for prior art search. We also study the influence of different parameters on the performance of the proposed method. The experimental results demonstrate that the proposed approach significantly improves the recall over a state-of-the-art baseline which uses the link-based structure of the citation graph but not the term distribution of the cited documents.},
journal = {Inf. Retr.},
month = oct,
pages = {412–429},
numpages = {18},
keywords = {Query expansion, Citation analysis, Patent retrieval}
}

@article{10.1007/s10791-013-9231-6,
author = {Magdy, Walid and Jones, Gareth J.},
title = {Studying Machine Translation Technologies for Large-Data CLIR Tasks: A Patent Prior-Art Search Case Study},
year = {2014},
issue_date = {October   2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {5–6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9231-6},
doi = {10.1007/s10791-013-9231-6},
abstract = {Prior-art search in patent retrieval is concerned with finding all existing patents relevant to a patent application. Since patents often appear in different languages, cross-language information retrieval (CLIR) is an essential component of effective patent search. In recent years machine translation (MT) has become the dominant approach to translation in CLIR. Standard MT systems focus on generating proper translations that are morphologically and syntactically correct. Development of effective MT systems of this type requires large training resources and high computational power for training and translation. This is an important issue for patent CLIR where queries are typically very long sometimes taking the form of a full patent application, meaning that query translation using MT systems can be very slow. However, in contrast to MT, the focus for information retrieval (IR) is on the conceptual meaning of the search words regardless of their surface form, or the linguistic structure of the output. Thus much of the complexity of MT is not required for effective CLIR. We present an adapted MT technique specifically designed for CLIR. In this method IR text pre-processing in the form of stop word removal and stemming are applied to the MT training corpus prior to the training phase. Applying this step leads to a significant decrease in the MT computational and training resources requirements. Experimental application of the new approach to the cross language patent retrieval task from CLEF-IP 2010 shows that the new technique to be up to 23 times faster than standard MT for query translations, while maintaining IR effectiveness statistically indistinguishable from standard MT when large training resources are used. Furthermore the new method is significantly better than standard MT when only limited translation training resources are available, which can be a significant issue for translation in specialized domains. The new MT technique also enables patent document translation in a practical amount of time with a resulting significant improvement in the retrieval effectiveness.},
journal = {Inf. Retr.},
month = oct,
pages = {492–519},
numpages = {28},
keywords = {Cross-language patent retrieval, Machine translation, Cross-language information retrieval, Large-data CLIR, Prior-art Patent search}
}

@article{10.1007/s10791-014-9243-x,
author = {Vuurens, Jeroen B. and Vries, Arjen P.},
title = {Distance Matters! Cumulative Proximity Expansions for Ranking Documents},
year = {2014},
issue_date = {August    2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9243-x},
doi = {10.1007/s10791-014-9243-x},
abstract = {In the information retrieval
process, functions that rank documents according to their estimated relevance to a query typically regard query terms as being independent. However, it is often the joint presence of query terms that is of interest to the user, which is overlooked when matching independent terms. One feature that can be used to express the relatedness of co-occurring terms is their proximity in text. In past research, models that are trained on the proximity information in a collection have performed better than models that are not estimated on data. We analyzed how co-occurring query terms can be used to estimate the relevance of documents based on their distance in text, which is used to extend a unigram ranking function with a proximity model that accumulates the scores of all occurring term combinations. This proximity model is more practical than existing models, since it does not require any co-occurrence statistics, it obviates the need to tune additional parameters, and has a retrieval speed close to competing models. We show that this approach is more robust than existing models, on both Web and newswire corpora, and on average performs equal or better than existing proximity models across collections.},
journal = {Inf. Retr.},
month = aug,
pages = {380–406},
numpages = {27},
keywords = {Query expansion, Term dependency, Term proximity}
}

@article{10.1007/s10791-014-9242-y,
author = {Hall, Mark M. and Fernando, Samuel and Clough, Paul D. and Soroa, Aitor and Agirre, Eneko and Stevenson, Mark},
title = {Evaluating Hierarchical Organisation Structures for Exploring Digital Libraries},
year = {2014},
issue_date = {August    2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9242-y},
doi = {10.1007/s10791-014-9242-y},
abstract = {Search boxes providing simple keyword-based search are insufficient when users have complex information needs or are unfamiliar with a collection, for example in large digital libraries. Browsing hierarchies can support these richer interactions, but many collections do not have a suitable hierarchy available. In this paper we present a number of approaches for automatically creating hierarchies and mapping items into them, including a novel technique which automatically adapts a Wikipedia-based taxonomy to the target collection. These approaches are applied to a large collection of cultural heritage items which is formed through the aggregation of other collections and for which no unified hierarchy is available. We investigate a number of novel user-evaluated metrics to quantify the hierarchies' quality and performance, showing that the proposed technique is preferred by users. From this we draw a number of conclusions as to what makes a hierarchy useful to the user.},
journal = {Inf. Retr.},
month = aug,
pages = {351–379},
numpages = {29},
keywords = {Evaluation, Exploratory search, Browsing, Hierarchical structures, Interactive information retrieval}
}

@article{10.1007/s10791-014-9241-z,
author = {Lee, Yeha and Lee, Jong-Hyeok},
title = {Identifying Top News Stories Based on Their Popularity in the Blogosphere},
year = {2014},
issue_date = {August    2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9241-z},
doi = {10.1007/s10791-014-9241-z},
abstract = {A huge volume of news stories are reported by various news channels, on a daily basis. Subscribing to all the stories and keeping track of the important ones day after day is very time-consuming. This paper proposes several approaches to identify important news stories. To this end, we take advantage of the blogosphere as an information source to evaluate the importance of news stories. Blogs reflect the diverse opinions of bloggers about news stories, and the attention that these stories receive can help estimate the importance of the stories. In this paper, we define the popularity of a news story in the blogosphere as the attention it attracts from users. We measure popularity of the stories in the blogosphere from two viewpoints: content and a timeline. In terms of content, we suggest several approaches to estimate language models for a news story and blog posts, and we evaluate the importance of the story using these language models. Furthermore, we generate a temporal profile of a news story by exploring the timeline of blog posts related to the story, and evaluate its importance based on the temporal profile. We experimentally verify the effectiveness of the proposed approaches for identifying top news stories.},
journal = {Inf. Retr.},
month = aug,
pages = {326–350},
numpages = {25},
keywords = {Blog retrieval, Top news stories identification, Language model approach, Blogosphere}
}

@article{10.1007/s10791-014-9240-0,
author = {Roa-Valverde, Antonio J. and Sicilia, Miguel-Angel},
title = {A Survey of Approaches for Ranking on the Web of Data},
year = {2014},
issue_date = {August    2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-014-9240-0},
doi = {10.1007/s10791-014-9240-0},
abstract = {Ranking information resources is a task that usually happens within more complex workflows and that typically occurs in any form of information retrieval, being commonly implemented by Web search engines. By filtering and rating data, ranking strategies guide the navigation of users when exploring large volumes of information items. There exist a considerable number of ranking algorithms that follow different approaches focusing on different aspects of the complex nature of the problem, and reflecting the variety of strategies that are possible to apply. With the growth of the web of linked data, a new problem space for ranking algorithms has emerged, as the nature of the information items to be ranked is very different from the case of Web pages. As a consequence, existing ranking algorithms have been adapted to the case of Linked Data and some specific strategies have started to be proposed and implemented. Researchers and organizations deploying Linked Data solutions thus require an understanding of the applicability, characteristics and state of evaluation of ranking strategies and algorithms as applied to Linked Data. We present a classification that formalizes and contextualizes under a common terminology the problem of ranking Linked Data. In addition, an analysis and contrast of the similarities, differences and applicability of the different approaches is provided. We aim this work to be useful when comparing different approaches to ranking Linked Data and when implementing new algorithms.},
journal = {Inf. Retr.},
month = aug,
pages = {295–325},
numpages = {31},
keywords = {Semantic search, Semantic web data management, Information retrieval, Link analysis, Linked data, Ranking algorithms}
}

@article{10.1007/s10791-013-9229-0,
author = {Wolff, Daniel and Weyde, Tillman},
title = {Learning Music Similarity from Relative User Ratings},
year = {2014},
issue_date = {April     2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9229-0},
doi = {10.1007/s10791-013-9229-0},
abstract = {Computational modelling of music similarity is an increasingly important part of personalisation and optimisation in music information retrieval and research in music perception and cognition. The use of relative similarity ratings is a new and promising approach to modelling similarity that avoids well known problems with absolute ratings. In this article, we use relative ratings from the MagnaTagATune dataset with new and existing variants of state-of-the-art algorithms and provide the first comprehensive and rigorous evaluation of this approach. We compare metric learning based on support vector machines (SVMs) and metric-learning-to-rank (MLR), including a diagonal and a novel weighted variant, and relative distance learning with neural networks (RDNN). We further evaluate the effectiveness of different high and low level audio features and genre data, as well as dimensionality reduction methods, weighting of similarity ratings, and different sampling methods. Our results show that music similarity measures learnt on relative ratings can be significantly better than a standard Euclidian metric, depending on the choice of learning algorithm, feature sets and application scenario. MLR and SVM outperform DMLR and RDNN, while MLR with weighted ratings leads to no further performance gain. Timbral and music-structural features are most effective, and all features jointly are significantly better than any other combination of feature sets. Sharing audio clips (but not the similarity ratings) between test and training sets improves performance, in particular for the SVM-based methods, which is useful for some applications scenarios. A testing framework has been implemented in Matlab and made publicly available http://mi.soi.city.ac.uk/datasets/ir2012framework so that these results are reproducible.},
journal = {Inf. Retr.},
month = apr,
pages = {109–136},
numpages = {28},
keywords = {Relative similarity ratings, Music similarity, Metric learning to rank, Support vector machines, Metric learning, Neural networks}
}

@article{10.1007/s10791-013-9226-3,
author = {Aly, Robin and Demeester, Thomas and Robertson, Stephen},
title = {Probabilistic Models in IR and Their Relationships},
year = {2014},
issue_date = {April     2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9226-3},
doi = {10.1007/s10791-013-9226-3},
abstract = {A solid research path towards new information retrieval models is to further develop the theory behind existing models. A profound understanding of these models is therefore essential. In this paper, we revisit probability ranking principle (PRP)-based models, probability of relevance (PR) models, and language models, finding conceptual differences in their definition and interrelationships. The probabilistic model of the PRP has not been explicitly defined previously, but doing so leads to the formulation of two actual principles with different objectives. First, the belief probability ranking principle (BPRP), which considers uncertain relevance between known documents and the current query, and second, the popularity probability ranking principle (PPRP), which considers the probability of relevance of documents among multiple queries with the same features. Our analysis shows how some of the discussed PR models implement the BPRP or the PPRP while others do not. However, for some models the parameter estimation is challenging. Finally, language models are often presented as related to PR models. However, we find that language models differ from PR models in every aspect of a probabilistic model and the effectiveness of language models cannot be explained by the PRP.},
journal = {Inf. Retr.},
month = apr,
pages = {177–201},
numpages = {25},
keywords = {Probability ranking principle, Language models, Probability of relevance, Probabilistic models}
}

@article{10.1007/s10791-013-9225-4,
author = {Kocaba\c{s}, undefinedlker and Din\c{c}er, Bekir Taner and Karao\u{g}lan, Bahar},
title = {A Nonparametric Term Weighting Method for Information Retrieval Based on Measuring the Divergence from Independence},
year = {2014},
issue_date = {April     2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9225-4},
doi = {10.1007/s10791-013-9225-4},
abstract = {In this article, we introduce an out-of-the-box automatic term weighting method for information retrieval. The method is based on measuring the degree of divergence from independence of terms from documents in terms of their frequency of occurrence. Divergence from independence has a well-establish underling statistical theory. It provides a plain, mathematically tractable, and nonparametric way of term weighting, and even more it requires no term frequency normalization. Besides its sound theoretical background, the results of the experiments performed on TREC test collections show that its performance is comparable to that of the state-of-the-art term weighting methods in general. It is a simple but powerful baseline alternative to the state-of-the-art methods with its theoretical and practical aspects.},
journal = {Inf. Retr.},
month = apr,
pages = {153–176},
numpages = {24},
keywords = {Statistical dependence, Nonparametric index term weighting, Pearson's Chi-Square statistics, Information retrieval}
}

@article{10.1007/s10791-013-9224-5,
author = {Morid, Mohammad Amin and Shajari, Mehdi and Hashemi, Ali Reza},
title = {Defending Recommender Systems by Influence Analysis},
year = {2014},
issue_date = {April     2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9224-5},
doi = {10.1007/s10791-013-9224-5},
abstract = {Collaborative filtering (CF) is a popular method for personalizing product recommendations for e-commerce applications. In order to recommend a product to a user and predict that user's preference, CF utilizes product evaluation ratings of like-minded users. The process of finding like-minded users forms a social network among all users and each link between two users represents an implicit connection between them. Users having more connections with others are the most influential users. Attacking recommender systems is a new issue for these systems. Here, an attacker tries to manipulate a recommender system in order to change the recommendation output according to her wish. If an attacker succeeds, her profile is used over and over again by the recommender system, making her an influential user. In this study, we applied the established attack detection methods to the influential users, instead of the whole user set, to improve their attack detection performance. Experiments were conducted using the same settings previously used to test the established methods. The results showed that the proposed influence-based method had better detection performance and improved the stability of a recommender system for most attack scenarios. It performed considerably better than established detection methods for attacks that inserted low numbers of attack profiles (20---25 %).},
journal = {Inf. Retr.},
month = apr,
pages = {137–152},
numpages = {16},
keywords = {Social network, Attack detection, Data mining, Recommender system, Shilling attack}
}

@article{10.1007/s10791-013-9228-1,
author = {Zheng, Wei and Fang, Hui and Yao, Conglei and Wang, Min},
title = {Leveraging Integrated Information to Extract Query Subtopics for Search Result Diversification},
year = {2014},
issue_date = {February  2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9228-1},
doi = {10.1007/s10791-013-9228-1},
abstract = {Search result diversification aims to diversify search results to cover different query subtopics, i.e., pieces of relevant information. The state of the art diversification methods often explicitly model the diversity based on query subtopics, and their performance is closely related to the quality of subtopics. Most existing studies extracted query subtopics only from the unstructured data such as document collections. However, there exists a huge amount of information from structured data, which complements the information from the unstructured data. The structured data can provide valuable information about domain knowledge, but is currently under-utilized. In this article, we study how to leverage the integrated information from both structured and unstructured data to extract high quality subtopics for search result diversification. We first discuss how to extract subtopics from structured data. We then propose three methods to integrate structured and unstructured data. Specifically, the first method uses the structured data to guide the subtopic extraction from unstructured data, the second one uses the unstructured data to guide the extraction, and the last one first extracts the subtopics separately from two data sources and then combines those subtopics. Experimental results in both Enterprise and Web search domains show that the proposed methods are effective in extracting high quality subtopics from the integrated information, which can lead to better diversification performance.},
journal = {Inf. Retr.},
month = feb,
pages = {52–73},
numpages = {22},
keywords = {Unstructured data, Enterprise search, Structured data, Web search, Diversification, Query subtopics}
}

@article{10.1007/s10791-013-9227-2,
author = {Peetz, Maria-Hendrike and Meij, Edgar and Rijke, Maarten},
title = {Using Temporal Bursts for Query Modeling},
year = {2014},
issue_date = {February  2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9227-2},
doi = {10.1007/s10791-013-9227-2},
abstract = {We present an approach to query modeling that leverages the temporal distribution of documents in an initially retrieved set of documents. In news-related document collections such distributions tend to exhibit bursts. Here, we define a burst to be a time period where unusually many documents are published. In our approach we detect bursts in result lists returned for a query. We then model the term distributions of the bursts using a reduced result list and select its most descriptive terms. Finally, we merge the sets of terms obtained in this manner so as to arrive at a reformulation of the original query. For query sets that consist of both temporal and non-temporal queries, our query modeling approach incorporates an effective selection method of terms. We consistently and significantly improve over various baselines, such as relevance models, on both news collections and a collection of blog posts.},
journal = {Inf. Retr.},
month = feb,
pages = {74–108},
numpages = {35},
keywords = {Information retrieval, Query modeling, Temporal information retrieval}
}

@article{10.1007/s10791-013-9220-9,
author = {Brosseau-Villeneuve, Bernard and Nie, Jian-Yun and Kando, Noriko},
title = {Latent Word Context Model for Information Retrieval},
year = {2014},
issue_date = {February  2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9220-9},
doi = {10.1007/s10791-013-9220-9},
abstract = {The application of word sense disambiguation (WSD) techniques to information retrieval (IR) has yet to provide convincing retrieval results. Major obstacles to effective WSD in IR include coverage and granularity problems of word sense inventories, sparsity of document context, and limited information provided by short queries. In this paper, to alleviate these issues, we propose the construction of latent context models for terms using latent Dirichlet allocation. We propose building one latent context per word, using a well principled representation of local context based on word features. In particular, context words are weighted using a decaying function according to their distance to the target word, which is learnt from data in an unsupervised manner. The resulting latent features are used to discriminate word contexts, so as to constrict query's semantic scope. Consistent and substantial improvements, including on difficult queries, are observed on TREC test collections, and the techniques combines well with blind relevance feedback. Compared to traditional topic modeling, WSD and positional indexing techniques, the proposed retrieval model is more effective and scales well on large-scale collections.},
journal = {Inf. Retr.},
month = feb,
pages = {21–51},
numpages = {31},
keywords = {Topic models, Retrieval models, Word sense disambiguation (WSD), Word context discrimination (WCD), Word context}
}

@article{10.1007/s10791-013-9219-2,
author = {Ruan, Yu-Xun and Lin, Hsuan-Tien and Tsai, Ming-Feng},
title = {Improving Ranking Performance with Cost-Sensitive Ordinal Classification via Regression},
year = {2014},
issue_date = {February  2014},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {17},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9219-2},
doi = {10.1007/s10791-013-9219-2},
abstract = {This paper proposes a novel ranking approach, cost-sensitive ordinal classification via regression (COCR), which respects the discrete nature of ordinal ranks in real-world data sets. In particular, COCR applies a theoretically sound method for reducing an ordinal classification to binary and solves the binary classification sub-tasks with point-wise regression. Furthermore, COCR allows us to specify mis-ranking costs to further improve the ranking performance; this ability is exploited by deriving a corresponding cost for a popular ranking criterion, expected reciprocal rank (ERR). The resulting ERR-tuned COCR boosts the benefits of the efficiency of using point-wise regression and the accuracy of top-rank prediction from the ERR criterion. Evaluations on four large-scale benchmark data sets, i.e., "Yahoo! Learning to Rank Challenge" and "Microsoft Learning to Rank," verify the significant superiority of COCR over commonly used regression approaches.},
journal = {Inf. Retr.},
month = feb,
pages = {1–20},
numpages = {20},
keywords = {Regression, List-wise ranking, Reduction, Cost-sensitive}
}

@article{10.1007/s10791-012-9217-9,
author = {Asadi, Nima and Lin, Jimmy},
title = {Document Vector Representations for Feature Extraction in Multi-Stage Document Ranking},
year = {2013},
issue_date = {December  2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9217-9},
doi = {10.1007/s10791-012-9217-9},
abstract = {We consider a multi-stage retrieval architecture consisting of a fast, "cheap" candidate generation stage, a feature extraction stage, and a more "expensive" reranking stage using machine-learned models. In this context, feature extraction can be accomplished using a document vector index, a mapping from document ids to document representations. We consider alternative organizations of such a data structure for efficient feature extraction: design choices include how document terms are organized, how complex term proximity features are computed, and how these structures are compressed. In particular, we propose a novel document-adaptive hashing scheme for compactly encoding term ids. The impact of alternative designs on both feature extraction speed and memory footprint is experimentally evaluated. Overall, results show that our architecture is comparable in speed to using a traditional positional inverted index but requires less memory overall, and offers additional advantages in terms of flexibility.},
journal = {Inf. Retr.},
month = dec,
pages = {747–768},
numpages = {22},
keywords = {Compression, Document stores, Learning to rank}
}

@article{10.1007/s10791-012-9216-x,
author = {Kato, Makoto P. and Sakai, Tetsuya and Tanaka, Katsumi},
title = {When Do People Use Query Suggestion? A Query Suggestion Log Analysis},
year = {2013},
issue_date = {December  2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9216-x},
doi = {10.1007/s10791-012-9216-x},
abstract = {Query suggestion, which enables the user to revise a query with a single click, has become one of the most fundamental features of Web search engines. However, it has not been clear what circumstances cause the user to turn to query suggestion. In order to investigate when and how the user uses query suggestion, we analyzed three kinds of data sets obtained from a major commercial Web search engine, comprising approximately 126 million unique queries, 876 million query suggestions and 306 million action patterns of users. Our analysis shows that query suggestions are often used (1) when the original query is a rare query, (2) when the original query is a single-term query, (3) when query suggestions are unambiguous, (4) when query suggestions are generalizations or error corrections of the original query, and (5) after the user has clicked on several URLs in the first search result page. Our results suggest that search engines should provide better assistance especially when rare or single-term queries are input, and that they should dynamically provide query suggestions according to the searcher's current state.},
journal = {Inf. Retr.},
month = dec,
pages = {725–746},
numpages = {22},
keywords = {Query log analysis, Web search, Query suggestion}
}

@article{10.1007/s10791-012-9214-z,
author = {Bellog\'{\i}n, Alejandro and Wang, Jun and Castells, Pablo},
title = {Bridging Memory-Based Collaborative Filtering and Text Retrieval},
year = {2013},
issue_date = {December  2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9214-z},
doi = {10.1007/s10791-012-9214-z},
abstract = {When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users' preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived. In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user's ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering.},
journal = {Inf. Retr.},
month = dec,
pages = {697–724},
numpages = {28},
keywords = {Text retrieval models, Collaborative filtering, Recommender systems}
}

@article{10.1007/s10791-012-9213-0,
author = {Formoso, Vreixo and Fern\'{a}ndez, Diego and Cacheda, Fidel and Carneiro, Victor},
title = {Using Rating Matrix Compression Techniques to Speed up Collaborative Recommendations},
year = {2013},
issue_date = {December  2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9213-0},
doi = {10.1007/s10791-012-9213-0},
abstract = {Collaborative filtering is a popular recommendation technique. Although researchers have focused on the accuracy of the recommendations, real applications also need efficient algorithms. An index structure can be used to store the rating matrix and compute recommendations very fast. In this paper we study how compression techniques can reduce the size of this index structure and, at the same time, speed up recommendations. We show how coding techniques commonly used in Information Retrieval can be effectively applied to collaborative filtering, reducing the matrix size up to 75 %, and almost doubling the recommendation speed. Additionally, we propose a novel identifier reassignment technique, that achieves high compression rates, reducing by 40 % the size of an already compressed matrix. It is a very simple approach based on assigning the smallest identifiers to the items and users with the highest number of ratings, and it can be efficiently computed using a two pass indexing. The usage of the proposed compression techniques can significantly reduce the storage and time costs of recommender systems, which are two important factors in many real applications.},
journal = {Inf. Retr.},
month = dec,
pages = {680–696},
numpages = {17},
keywords = {Collaborative filtering, Recommender systems, Rating matrix compression, Identifier assignment}
}

@article{10.1007/s10791-012-9212-1,
author = {Arampatzis, Avi and Efraimidis, Pavlos S. and Drosatos, George},
title = {A Query Scrambler for Search Privacy on the Internet},
year = {2013},
issue_date = {December  2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9212-1},
doi = {10.1007/s10791-012-9212-1},
abstract = {We propose a method for search privacy on the Internet, focusing on enhancing plausible deniability against search engine query-logs. The method approximates the target search results, without submitting the intended query and avoiding other exposing queries, by employing sets of queries representing more general concepts. We model the problem theoretically, and investigate the practical feasibility and effectiveness of the proposed solution with a set of real queries with privacy issues on a large web collection. The findings may have implications for other IR research areas, such as query expansion and fusion in meta-search. Finally, we discuss ideas for privacy, such as k-anonymity, and how these may be applied to search tasks.},
journal = {Inf. Retr.},
month = dec,
pages = {657–679},
numpages = {23},
keywords = {Search privacy, WordNet, Fusion, Query scrambler}
}

@article{10.1007/s10791-012-9210-3,
author = {Litvak, Marina and Last, Mark},
title = {Cross-Lingual Training of Summarization Systems Using Annotated Corpora in a Foreign Language},
year = {2013},
issue_date = {October   2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9210-3},
doi = {10.1007/s10791-012-9210-3},
abstract = {The increasing trend of cross-border globalization and acculturation requires text summarization techniques to work equally well for multiple languages. However, only some of the automated summarization methods can be defined as "language-independent," i.e., not based on any language-specific knowledge. Such methods can be used for multilingual summarization, defined in Mani (Automatic summarization. Natural language processing. John Benjamins Publishing Company, Amsterdam, 2001 ) as "processing several languages, with a summary in the same language as input", but, their performance is usually unsatisfactory due to the exclusion of language-specific knowledge. Moreover, supervised machine learning approaches need training corpora in multiple languages that are usually unavailable for rare languages, and their creation is a very expensive and labor-intensive process. In this article, we describe cross-lingual methods for training an extractive single-document text summarizer called MUSE (MUltilingual Sentence Extractor)--a supervised approach, based on the linear optimization of a rich set of sentence ranking measures using a Genetic Algorithm. We evaluated MUSE's performance on documents in three different languages: English, Hebrew, and Arabic using several training scenarios. The summarization quality was measured using ROUGE-1 and ROUGE-2 Recall metrics. The results of the extensive comparative analysis showed that the performance of MUSE was better than that of the best known multilingual approach (TextRank) in all three languages. Moreover, our experimental results suggest that using the same sentence ranking model across languages results in a reasonable summarization quality, while saving considerable annotation efforts for the end-user. On the other hand, using parallel corpora generated by machine translation tools may improve the performance of a MUSE model trained on a foreign language. Comparative evaluation of an alternative optimization technique--Multiple Linear Regression--justifies the use of a Genetic Algorithm.},
journal = {Inf. Retr.},
month = oct,
pages = {629–656},
numpages = {28},
keywords = {Cross-lingual training, Multilingual summarization, Genetic Algorithm}
}

@article{10.1007/s10791-012-9209-9,
author = {Macdonald, Craig and Santos, Rodrygo L. and Ounis, Iadh},
title = {The Whens and Hows of Learning to Rank for Web Search},
year = {2013},
issue_date = {October   2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9209-9},
doi = {10.1007/s10791-012-9209-9},
abstract = {Web search engines are increasingly deploying many features, combined using learning to rank techniques. However, various practical questions remain concerning the manner in which learning to rank should be deployed. For instance, a sample of documents with sufficient recall is used, such that re-ranking of the sample by the learned model brings the relevant documents to the top. However, the properties of the document sample such as when to stop ranking--i.e. its minimum effective size--remain unstudied. Similarly, effective listwise learning to rank techniques minimise a loss function corresponding to a standard information retrieval evaluation measure. However, the appropriate choice of how to calculate the loss function--i.e. the choice of the learning evaluation measure and the rank depth at which this measure should be calculated--are as yet unclear. In this paper, we address all of these issues by formulating various hypotheses and research questions, before performing exhaustive experiments using multiple learning to rank techniques and different types of information needs on the ClueWeb09 and LETOR corpora. Among many conclusions, we find, for instance, that the smallest effective sample for a given query set is dependent on the type of information need of the queries, the document representation used during sampling and the test evaluation measure. As the sample size is varied, the selected features markedly change--for instance, we find that the link analysis features are favoured for smaller document samples. Moreover, despite reflecting a more realistic user model, the recently proposed ERR measure is not as effective as the traditional NDCG as a learning loss function. Overall, our comprehensive experiments provide the first empirical derivation of best practices for learning to rank deployments.},
journal = {Inf. Retr.},
month = oct,
pages = {584–628},
numpages = {45},
keywords = {Web search, Evaluation, Learning to rank, Loss function, Sample size, Document representations}
}

@article{10.1007/s10791-012-9207-y,
author = {Aly, Robin and Doherty, Aiden and Hiemstra, Djoerd and Jong, Franciska and Smeaton, Alan F.},
title = {The Uncertain Representation Ranking Framework for Concept-Based Video Retrieval},
year = {2013},
issue_date = {October   2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9207-y},
doi = {10.1007/s10791-012-9207-y},
abstract = {Concept based video retrieval often relies on imperfect and uncertain concept detectors. We propose a general ranking framework to define effective and robust ranking functions, through explicitly addressing detector uncertainty. It can cope with multiple concept-based representations per video segment and it allows the re-use of effective text retrieval functions which are defined on similar representations. The final ranking status value is a weighted combination of two components: the expected score of the possible scores, which represents the risk-neutral choice, and the scores' standard deviation, which represents the risk or opportunity that the score for the actual representation is higher. The framework consistently improves the search performance in the shot retrieval task and the segment retrieval task over several baselines in five TRECVid collections and two collections which use simulated detectors of varying performance.},
journal = {Inf. Retr.},
month = oct,
pages = {557–583},
numpages = {27},
keywords = {Representation uncertainty, Concept-based representation, Video retrieval}
}

@article{10.1007/s10791-013-9223-6,
author = {Sakai, Tetsuya and Kando, Noriko and Macdonald, Craig and Soboroff, Ian},
title = {Introduction to the Special Issue on Search Intents and Diversification},
year = {2013},
issue_date = {August    2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9223-6},
doi = {10.1007/s10791-013-9223-6},
journal = {Inf. Retr.},
month = aug,
pages = {427–428},
numpages = {2}
}

@article{10.1007/s10791-013-9221-8,
author = {Wang, Qinglei and Qian, Yanan and Song, Ruihua and Dou, Zhicheng and Zhang, Fan and Sakai, Tetsuya and Zheng, Qinghua},
title = {Mining Subtopics from Text Fragments for a Web Query},
year = {2013},
issue_date = {August    2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9221-8},
doi = {10.1007/s10791-013-9221-8},
abstract = {Web search queries are often ambiguous or faceted, and the task of identifying the major underlying senses and facets of queries has received much attention in recent years. We refer to this task as query subtopic mining. In this paper, we propose to use surrounding text of query terms in top retrieved documents to mine subtopics and rank them. We first extract text fragments containing query terms from different parts of documents. Then we group similar text fragments into clusters and generate a readable subtopic for each cluster. Based on the cluster and the language model trained from a query log, we calculate three features and combine them into a relevance score for each subtopic. Subtopics are finally ranked by balancing relevance and novelty. Our evaluation experiments with the NTCIR-9 INTENT Chinese Subtopic Mining test collection show that our method significantly outperforms a query log based method proposed by Radlinski et al. (2010) and a search result clustering based method proposed by Zeng et al. (2004) in terms of precision, I-rec, D-nDCG and D#-nDCG, the official evaluation metrics used at the NTCIR-9 INTENT task. Moreover, our generated subtopics are significantly more readable than those generated by the search result clustering method.},
journal = {Inf. Retr.},
month = aug,
pages = {484–503},
numpages = {20},
keywords = {Query intent, Intents ranking, Intent mining}
}

@article{10.1007/s10791-012-9218-8,
author = {Golbus, Peter B. and Aslam, Javed A. and Clarke, Charles L.},
title = {Increasing Evaluation Sensitivity to Diversity},
year = {2013},
issue_date = {August    2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9218-8},
doi = {10.1007/s10791-012-9218-8},
abstract = {Many queries have multiple interpretations; they are ambiguous or underspecified. This is especially true in the context of Web search. To account for this, much recent research has focused on creating systems that produce diverse ranked lists. In order to validate these systems, several new evaluation measures have been created to quantify diversity. Ideally, diversity evaluation measures would distinguish between systems by the amount of diversity in the ranked lists they produce. Unfortunately, diversity is also a function of the collection over which the system is run and a system's performance at ad-hoc retrieval. A ranked list built from a collection that does not cover multiple subtopics cannot be diversified; neither can a ranked list that contains no relevant documents. To ensure that we are assessing systems by their diversity, we develop (1) a family of evaluation measures that take into account the diversity of the collection and (2) a meta-evaluation measure that explicitly controls for performance. We demonstrate experimentally that our new measures can achieve substantial improvements in sensitivity to diversity without reducing discriminative power.},
journal = {Inf. Retr.},
month = aug,
pages = {530–555},
numpages = {26},
keywords = {Diversity, Diversity difficulty, Evaluation}
}

@article{10.1007/s10791-012-9215-y,
author = {Wang, Chieh-Jen and Lin, Yung-Wei and Tsai, Ming-Feng and Chen, Hsin-Hsi},
title = {Mining Subtopics from Different Aspects for Diversifying Search Results},
year = {2013},
issue_date = {August    2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9215-y},
doi = {10.1007/s10791-012-9215-y},
abstract = {User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users' various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is -nDCG@5 0.307, IA-P@5 0.121, and #-nDCG@5 0.214 on the TREC09, as well as -nDCG@10 0.421, IA-P@10 0.201, and #-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users' search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics.},
journal =

license_type: permissive

branch_name: refs/heads/main

extension: bib