text:
{Inf. Retr.},
month = aug,
pages = {452–483},
numpages = {32},
keywords = {Search result re-ranking, Subtopic mining, Diversified retrieval}
}

@article{10.1007/s10791-012-9211-2,
author = {Santos, Rodrygo L. and Macdonald, Craig and Ounis, Iadh},
title = {Learning to Rank Query Suggestions for Adhoc and Diversity Search},
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/s10791-012-9211-2},
doi = {10.1007/s10791-012-9211-2},
abstract = {Query suggestions have become pervasive in modern web search, as a mechanism to guide users towards a better representation of their information need. In this article, we propose a ranking approach for producing effective query suggestions. In particular, we devise a structured representation of candidate suggestions mined from a query log that leverages evidence from other queries with a common session or a common click. This enriched representation not only helps overcome data sparsity for long-tail queries, but also leads to multiple ranking criteria, which we integrate as features for learning to rank query suggestions. To validate our approach, we build upon existing efforts for web search evaluation and propose a novel framework for the quantitative assessment of query suggestion effectiveness. Thorough experiments using publicly available data from the TREC Web track show that our approach provides effective suggestions for adhoc and diversity search.},
journal = {Inf. Retr.},
month = aug,
pages = {429–451},
numpages = {23},
keywords = {Diversity, Learning to rank, Web search, Relevance, Query suggestions}
}

@article{10.1007/s10791-012-9208-x,
author = {Sakai, Tetsuya and Song, Ruihua},
title = {Diversified Search Evaluation: Lessons from the NTCIR-9 INTENT Task},
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/s10791-012-9208-x},
doi = {10.1007/s10791-012-9208-x},
abstract = {The evaluation of diversified web search results is a relatively new research topic and is not as well-understood as the time-honoured evaluation methodology of traditional IR based on precision and recall. In diversity evaluation, one topic may have more than one  intent , and systems are expected to balance relevance and diversity. The recent NTCIR-9 evaluation workshop launched a new task called INTENT which included a diversified web search subtask that differs from the TREC web diversity task in several aspects: the choice of evaluation metrics, the use of intent popularity and per-intent graded relevance, and the use of topic sets that are twice as large as those of TREC. The objective of this study is to examine whether these differences are useful, using the actual data recently obtained from the NTCIR-9 INTENT task. Our main experimental findings are: (1) The  $$hbox{D},sharp$$  evaluation framework used at NTCIR provides more "intuitive" and statistically reliable results than Intent-Aware Expected Reciprocal Rank; (2) Utilising both intent popularity and per-intent graded relevance as is done at NTCIR tends to improve discriminative power, particularly for  $$hbox{D},sharp$$ -nDCG; and (3) Reducing the topic set size, even by just 10 topics, can affect not only significance testing but also the entire system ranking; when 50 topics are used (as in TREC) instead of 100 (as in NTCIR), the system ranking can be substantially different from the original ranking and the discriminative power can be halved. These results suggest that the directions being explored at NTCIR are valuable.},
journal = {Inf. Retr.},
month = aug,
pages = {504–529},
numpages = {26},
keywords = {Intents, Test collections, Search result diversification, NTCIR, Diversity, Web search, TREC, Evaluation}
}

@article{10.1007/s10791-012-9202-3,
author = {Goyal, Pawan and Behera, Laxmidhar and Mcginnity, T. M.},
title = {A Novel Neighborhood Based Document Smoothing Model for Information Retrieval},
year = {2013},
issue_date = {June      2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9202-3},
doi = {10.1007/s10791-012-9202-3},
abstract = {In this paper, a novel neighborhood based document smoothing  model for information retrieval  has been proposed. Lexical association between terms is used to provide a context sensitive indexing weight to the document terms, i.e. the term weights are redistributed based on the lexical association with the context words. A generalized retrieval framework has been presented and it has been shown that the vector space model (VSM), divergence from randomness (DFR), Okapi Best Matching 25 (BM25) and the language model (LM) based retrieval frameworks are special cases of this generalized framework. Being proposed in the generalized retrieval framework, the neighborhood based document smoothing model is applicable to all the indexing models that use the term-document frequency scheme. The proposed smoothing model is as efficient as the baseline retrieval frameworks at runtime. Experiments over the TREC datasets show that the neighborhood based document smoothing model consistently improves the retrieval performance of VSM, DFR, BM25 and LM and the improvements are statistically significant.},
journal = {Inf. Retr.},
month = jun,
pages = {391–425},
numpages = {35},
keywords = {Document smoothing, Information retrieval, Lexical association}
}

@article{10.1007/s10791-012-9201-4,
author = {Cetintas, Suleyman and Chen, Datong and Si, Luo},
title = {Forecasting User Visits for Online Display Advertising},
year = {2013},
issue_date = {June      2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9201-4},
doi = {10.1007/s10791-012-9201-4},
abstract = {Online display advertising is a multi-billion dollar industry where advertisers promote their products to users by having publishers display their advertisements on popular Web pages. An important problem in online advertising is how to forecast the number of user visits for a Web page during a particular period of time. Prior research addressed the problem by using traditional time-series forecasting techniques on historical data of user visits; (e.g., via a single regression model built for forecasting based on historical data for all Web pages) and did not fully explore the fact that different types of Web pages and different time stamps have different patterns of user visits. In this paper, we propose a series of probabilistic latent class models to automatically learn the underlying user visit patterns among multiple Web pages and multiple time stamps. The last (and the most effective) proposed model identifies latent groups/classes of (i) Web pages and (ii) time stamps with similar user visit patterns, and learns a specialized forecast model for each latent Web page and time stamp class. Compared with a single regression model as well as several other baselines, the proposed latent class model approach has the capability of differentiating the importance of different types of information across different classes of Web pages and time stamps, and therefore has much better modeling flexibility. An extensive set of experiments along with detailed analysis carried out on real-world data from Yahoo! demonstrates the advantage of the proposed latent class models in forecasting online user visits in online display advertising.},
journal = {Inf. Retr.},
month = jun,
pages = {369–390},
numpages = {22},
keywords = {Display advertising, Forecasting, User visits}
}

@article{10.1007/s10791-012-9200-5,
author = {Vuli\'{c}, Ivan and Smet, Wim and Moens, Marie-Francine},
title = {Cross-Language Information Retrieval Models Based on Latent Topic Models Trained with Document-Aligned Comparable Corpora},
year = {2013},
issue_date = {June      2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9200-5},
doi = {10.1007/s10791-012-9200-5},
abstract = {In this paper, we study different applications of cross-language latent topic models trained on comparable corpora. The first focus lies on the task of cross-language information retrieval (CLIR). The Bilingual Latent Dirichlet allocation model (BiLDA) allows us to create an interlingual, language-independent representation of both queries and documents. We construct several BiLDA-based document models for CLIR, where no additional translation resources are used. The second focus lies on the methods for extracting translation candidates and semantically related words using only per-topic word distributions of the cross-language latent topic model. As the main contribution, we combine the two former steps, blending the evidences from the per-document topic distributions and the per-topic word distributions of the topic model with the knowledge from the extracted lexicon. We design and evaluate the novel evidence-rich statistical model for CLIR, and prove that such a model, which combines various (only internal) evidences, obtains the best scores for experiments performed on the standard test collections of the CLEF 2001---2003 campaigns. We confirm these findings in an alternative evaluation, where we automatically generate queries and perform the known-item search on a test subset of Wikipedia articles. The main importance of this work lies in the fact that we train translation resources from comparable document-aligned corpora and provide novel CLIR statistical models that exhaustively exploit as many cross-lingual clues as possible in the quest for better CLIR results, without use of any additional external resources such as parallel corpora or machine-readable dictionaries.},
journal = {Inf. Retr.},
month = jun,
pages = {331–368},
numpages = {38},
keywords = {Cross-language information retrieval, Unsupervised cross-language lexicon extraction, Evidence-rich retrieval models, Probabilistic latent topic models}
}

@article{10.1007/s10791-012-9198-8,
author = {Karimzadehgan, Maryam and Zhai, Chengxiang},
title = {A Learning Approach to Optimizing Exploration---Exploitation Tradeoff in Relevance Feedback},
year = {2013},
issue_date = {June      2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9198-8},
doi = {10.1007/s10791-012-9198-8},
abstract = {Relevance feedback is an effective technique for improving search accuracy in interactive information retrieval. In this paper, we study an interesting optimization problem in interactive feedback that aims at optimizing the tradeoff between presenting search results with the highest immediate utility to a user (but not necessarily most useful for collecting feedback information) and presenting search results with the best potential for collecting useful feedback information (but not necessarily the most useful documents from a user's perspective). Optimizing such an exploration---exploitation tradeoff is key to the optimization of the overall utility of relevance feedback to a user in the entire session of relevance feedback. We formally frame this tradeoff as a problem of optimizing the diversification of search results since relevance judgments on more diversified results have been shown to be more useful for relevance feedback. We propose a machine learning approach to adaptively optimizing the diversification of search results for each query so as to optimize the overall utility in an entire session. Experiment results on three representative retrieval test collections show that the proposed learning approach can effectively optimize the exploration---exploitation tradeoff and outperforms the traditional relevance feedback approach which only does exploitation without exploration.},
journal = {Inf. Retr.},
month = jun,
pages = {307–330},
numpages = {24},
keywords = {Feedback, Interactive retrieval models, Diversification, User modeling}
}

@article{10.1007/s10791-013-9222-7,
author = {Lease, Matthew and Yilmaz, Emine},
title = {Crowdsourcing for Information Retrieval: Introduction to the Special Issue},
year = {2013},
issue_date = {April     2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-013-9222-7},
doi = {10.1007/s10791-013-9222-7},
abstract = {This introduction to the special issue summarizes and contextualizes six novel research contributions at the intersection of information retrieval (IR) and crowdsourcing (also overlapping crowdsourcing's closely-related sibling, human computation). Several of the papers included in this special issue represent deeper investigations into research topics for which earlier stages of the authors' research were disseminated at crowdsourcing workshops at SIGIR and WSDM conferences, as well as at the NIST TREC conference. Since the first proposed use of crowdsourcing for IR in 2008, interest in this area has quickly accelerated and led to three workshops, an ongoing NIST TREC track, and a great variety of published papers, talks, and tutorials. We briefly summarize the area in order to help situate the contributions appearing in this special issue. We also discuss some broader current trends and issues in crowdsourcing which bear upon its use in IR and other fields.},
journal = {Inf. Retr.},
month = apr,
pages = {91–100},
numpages = {10},
keywords = {Search evaluation, Human computation, Crowdsourcing}
}

@article{10.1007/s10791-012-9206-z,
author = {Zuccon, Guido and Leelanupab, Teerapong and Whiting, Stewart and Yilmaz, Emine and Jose, Joemon M. and Azzopardi, Leif},
title = {Crowdsourcing Interactions: Using Crowdsourcing for Evaluating Interactive Information Retrieval Systems},
year = {2013},
issue_date = {April     2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9206-z},
doi = {10.1007/s10791-012-9206-z},
abstract = {In the field of information retrieval (IR), researchers and practitioners are often faced with a demand for valid approaches to evaluate the performance of retrieval systems. The Cranfield experiment paradigm has been dominant for the in-vitro evaluation of IR systems. Alternative to this paradigm, laboratory-based user studies have been widely used to evaluate interactive information retrieval (IIR) systems, and at the same time investigate users' information searching behaviours. Major drawbacks of laboratory-based user studies for evaluating IIR systems include the high monetary and temporal costs involved in setting up and running those experiments, the lack of heterogeneity amongst the user population and the limited scale of the experiments, which usually involve a relatively restricted set of users. In this paper, we propose an alternative experimental methodology to laboratory-based user studies. Our novel experimental methodology uses a crowdsourcing platform as a means of engaging study participants. Through crowdsourcing, our experimental methodology can capture user interactions and searching behaviours at a lower cost, with more data, and within a shorter period than traditional laboratory-based user studies, and therefore can be used to assess the performances of IIR systems. In this article, we show the characteristic differences of our approach with respect to traditional IIR experimental and evaluation procedures. We also perform a use case study comparing crowdsourcing-based evaluation with laboratory-based evaluation of IIR systems, which can serve as a tutorial for setting up crowdsourcing-based IIR evaluations.},
journal = {Inf. Retr.},
month = apr,
pages = {267–305},
numpages = {39},
keywords = {Interactive IR evaluation, Crowdsourcing evaluation, Interactions}
}

@article{10.1007/s10791-012-9205-0,
author = {Kazai, Gabriella and Kamps, Jaap and Milic-Frayling, Natasa},
title = {An Analysis of Human Factors and Label Accuracy in Crowdsourcing Relevance Judgments},
year = {2013},
issue_date = {April     2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9205-0},
doi = {10.1007/s10791-012-9205-0},
abstract = {Crowdsourcing relevance judgments for the evaluation of search engines is used increasingly to overcome the issue of scalability that hinders traditional approaches relying on a fixed group of trusted expert judges. However, the benefits of crowdsourcing come with risks due to the engagement of a self-forming group of individuals--the crowd, motivated by different incentives, who complete the tasks with varying levels of attention and success. This increases the need for a careful design of crowdsourcing tasks that attracts the right crowd for the given task and promotes quality work. In this paper, we describe a series of experiments using Amazon's Mechanical Turk, conducted to explore the `human' characteristics of the crowds involved in a relevance assessment task. In the experiments, we vary the level of pay offered, the effort required to complete a task and the qualifications required of the workers. We observe the effects of these variables on the quality of the resulting relevance labels, measured based on agreement with a gold set, and correlate them with self-reported measures of various human factors. We elicit information from the workers about their motivations, interest and familiarity with the topic, perceived task difficulty, and satisfaction with the offered pay. We investigate how these factors combine with aspects of the task design and how they affect the accuracy of the resulting relevance labels. Based on the analysis of 960 HITs and 2,880 HIT assignments resulting in 19,200 relevance labels, we arrive at insights into the complex interaction of the observed factors and provide practical guidelines to crowdsourcing practitioners. In addition, we highlight challenges in the data analysis that stem from the peculiarity of the crowdsourcing environment where the sample of individuals engaged in specific work conditions are inherently influenced by the conditions themselves.},
journal = {Inf. Retr.},
month = apr,
pages = {138–178},
numpages = {41},
keywords = {Study of human factors, Crowdsourcing, Relevance judgments}
}

@article{10.1007/s10791-012-9204-1,
author = {Alonso, Omar},
title = {Implementing Crowdsourcing-Based Relevance Experimentation: An Industrial Perspective},
year = {2013},
issue_date = {April     2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9204-1},
doi = {10.1007/s10791-012-9204-1},
abstract = {Crowdsourcing has emerged as a viable platform for conducting different types of relevance evaluation. The main reason behind this trend is that it makes possible to conduct experiments extremely fast, with good results at a low cost. However, like in any experiment, there are several implementation details that would make an experiment work or fail. To gather useful results, clear instructions, user interface guidelines, content quality, inter-rater agreement metrics, work quality, and worker feedback are important characteristics of a successful crowdsourcing experiment. Furthermore, designing and implementing experiments that require thousands or millions of labels is different than conducting small scale research investigations. In this paper we outline a framework for conducting continuous crowdsourcing experiments, emphasizing aspects that should be of importance for all sorts of tasks. We illustrate the value of characteristics that can impact the overall outcome using examples based on TREC, INEX, and Wikipedia data sets.},
journal = {Inf. Retr.},
month = apr,
pages = {101–120},
numpages = {20},
keywords = {Crowdsourcing, Experiment design, Methodology, Relevance assessment & evaluation}
}

@article{10.1007/s10791-012-9203-2,
author = {Munro, Robert},
title = {Crowdsourcing and the Crisis-Affected Community},
year = {2013},
issue_date = {April     2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9203-2},
doi = {10.1007/s10791-012-9203-2},
abstract = {This article reports on Mission 4636, a real-time humanitarian crowdsourcing initiative that processed 80,000 text messages (SMS) sent from within Haiti following the 2010 earthquake. It was the first time that crowdsourcing (microtasking) had been used for international relief efforts, and is the largest deployment of its kind to date. This article presents the first full report and analysis of the initiative looking at the accuracy and timeliness in creating structured data from the messages and the collaborative nature of the process. Contrary to all previous papers, studies and media reports about Mission 4636, which have typically chosen to exclude empirical analyses and the involvement of the Haitian population, it is found that the greatest volume, speed and accuracy in information processing was by Haitian nationals, the Haitian diaspora, and those working closest with them, and that no new technologies played a significant role. It is concluded that international humanitarian organizations have been wrongly credited for large-scale information processing initiatives (here and elsewhere) and that for the most part they were largely just witnesses to crisis-affected communities bootstrapping their own recovery through communications technologies. The particular focus is on the role of the diaspora, an important population that are increasingly able to contribute to response efforts thanks to their increased communication potential. It is recommended that future humanitarian deployments of crowdsourcing focus on information processing within the populations they serve, engaging those with crucial local knowledge wherever they happen to be in the world.},
journal = {Inf. Retr.},
month = apr,
pages = {210–266},
numpages = {57},
keywords = {Microtasking, Crowdsourcing, Haiti, SMS}
}

@article{10.1007/s10791-012-9186-z,
author = {Mccreadie, Richard and Macdonald, Craig and Ounis, Iadh},
title = {Identifying Top News Using Crowdsourcing},
year = {2013},
issue_date = {April     2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9186-z},
doi = {10.1007/s10791-012-9186-z},
abstract = {The influential Text REtrieval Conference (TREC) retrieval conference has always relied upon specialist assessors or occasionally participating groups to create relevance judgements for the tracks that it runs. Recently however, crowdsourcing has been championed as a cheap, fast and effective alternative to traditional TREC-like assessments. In 2010, TREC tracks experimented with crowdsourcing for the very first time. In this paper, we report our successful experience in creating relevance assessments for the TREC Blog track 2010 top news stories task using crowdsourcing. In particular, we crowdsourced both real-time newsworthiness assessments for news stories as well as traditional relevance assessments for blog posts. We conclude that crowdsourcing not only appears to be a feasible, but also cheap and fast means to generate relevance assessments. Furthermore, we detail our experiences running the crowdsourced evaluation of the TREC Blog track, discuss the lessons learned, and provide best practices.},
journal = {Inf. Retr.},
month = apr,
pages = {179–209},
numpages = {31},
keywords = {Blog, Relevance assessment, TREC, Top news, Crowdsourcing}
}

@article{10.1007/s10791-011-9181-9,
author = {Eickhoff, Carsten and Vries, Arjen P.},
title = {Increasing Cheat Robustness of Crowdsourcing Tasks},
year = {2013},
issue_date = {April     2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9181-9},
doi = {10.1007/s10791-011-9181-9},
abstract = {Crowdsourcing successfully strives to become a widely used means of collecting large-scale scientific corpora. Many research fields, including Information Retrieval, rely on this novel way of data acquisition. However, it seems to be undermined by a significant share of workers that are primarily interested in producing quick generic answers rather than correct ones in order to optimise their time-efficiency and, in turn, earn more money. Recently, we have seen numerous sophisticated schemes of identifying such workers. Those, however, often require additional resources or introduce artificial limitations to the task. In this work, we take a different approach by investigating means of a priori making crowdsourced tasks more resistant against cheaters.},
journal = {Inf. Retr.},
month = apr,
pages = {121–137},
numpages = {17},
keywords = {Human factors, Crowdsourcing, User experiments, Stability}
}

@article{10.1007/s10791-012-9197-9,
author = {Hofmann, Katja and Whiteson, Shimon and Rijke, Maarten},
title = {Balancing Exploration and Exploitation in Listwise and Pairwise Online Learning to Rank for Information Retrieval},
year = {2013},
issue_date = {February  2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9197-9},
doi = {10.1007/s10791-012-9197-9},
abstract = {As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank, retrieval systems can learn directly from implicit feedback inferred from user interactions. In such an online setting, algorithms must obtain feedback for effective learning while simultaneously utilizing what has already been learned to produce high quality results. We formulate this challenge as an exploration---exploitation dilemma and propose two methods for addressing it. By adding mechanisms for balancing exploration and exploitation during learning, each method extends a state-of-the-art learning to rank method, one based on listwise learning and the other on pairwise learning. Using a recently developed simulation framework that allows assessment of online performance, we empirically evaluate both methods. Our results show that balancing exploration and exploitation can substantially and significantly improve the online retrieval performance of both listwise and pairwise approaches. In addition, the results demonstrate that such a balance affects the two approaches in different ways, especially when user feedback is noisy, yielding new insights relevant to making online learning to rank effective in practice.},
journal = {Inf. Retr.},
month = feb,
pages = {63–90},
numpages = {28},
keywords = {Information retrieval, Implicit feedback, Learning to rank}
}

@article{10.1007/s10791-012-9195-y,
author = {O'Hare, Neil and Murdock, Vanessa},
title = {Modeling Locations with Social Media},
year = {2013},
issue_date = {February  2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9195-y},
doi = {10.1007/s10791-012-9195-y},
abstract = {In this paper we focus on the locations explicit and implicit in users descriptions of their surroundings. We propose a statistical language modeling approach to identifying locations in arbitrary text, and investigate several ways to estimate the models, based on the term frequency and the user frequency. The geotagged public photos in Flickr serve as a convenient ground truth. Our results show that we can predict location within a one kilometer by one kilometer cell with 17 % accuracy, and within a three kilometer radius around such a one kilometer cell with 40 % accuracy, using only a photo's tags. This is significantly better than the state of the art. Further we examine several estimation strategies that leverage the physical proximity of places, and show that for sparsely represented locations, smoothing from the immediate neighborhood improves results. We also show that estimation strategies based on user frequency are much more reliable than approaches based on the raw term frequency.},
journal = {Inf. Retr.},
month = feb,
pages = {30–62},
numpages = {33},
keywords = {Geographic context, Geotagging, Flickr, User-generated content, Language models}
}

@article{10.1007/s10791-012-9194-z,
author = {Shakery, Azadeh and Zhai, Chengxiang},
title = {Leveraging Comparable Corpora for Cross-Lingual Information Retrieval in Resource-Lean Language Pairs},
year = {2013},
issue_date = {February  2013},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {16},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9194-z},
doi = {10.1007/s10791-012-9194-z},
abstract = {Cross-language information retrieval (CLIR) has so far been studied with the assumption that some rich linguistic resources such as bilingual dictionaries or parallel corpora are available. But creation of such high quality resources is labor-intensive and they are not always at hand. In this paper we investigate the feasibility of using only comparable corpora for CLIR, without relying on other linguistic resources. Comparable corpora are text documents in different languages that cover similar topics and are often naturally attainable (e.g., news articles published in different languages at the same time period). We adapt an existing cross-lingual word association mining method and incorporate it into a language modeling approach to cross-language retrieval. We investigate different strategies for estimating the target query language models. Our evaluation results on the TREC Arabic---English cross-lingual data show that the proposed method is effective for the CLIR task, demonstrating that it is feasible to perform cross-lingual information retrieval with just comparable corpora.},
journal = {Inf. Retr.},
month = feb,
pages = {1–29},
numpages = {29},
keywords = {Probabilistic propagation, Comparable corpora, Language models, Cross-language information retrieval}
}

@article{10.1007/s10791-012-9192-1,
author = {Ali, M. Sadek and Consens, Mariano and Lalmas, Mounia},
title = {Extended Structural Relevance Framework: A Framework for Evaluating Structured Document Retrieval},
year = {2012},
issue_date = {December  2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9192-1},
doi = {10.1007/s10791-012-9192-1},
abstract = {A structured document retrieval (SDR) system aims to minimize the effort users spend to locate relevant information by retrieving parts of documents. To evaluate the range of SDR tasks, from element to passage to tree retrieval, numerous task-specific measures have been proposed. This has resulted in SDR evaluation measures that cannot easily be compared with respect to each other and across tasks. In previous work, we defined the SDR task of tree retrieval where passage and element are special cases. In this paper, we look in greater detail into tree retrieval to identify the main components of SDR evaluation: relevance, navigation, and redundancy. Our goal is to evaluate SDR within a single probabilistic framework based on these components. This framework, called Extended Structural Relevance (ESR), calculates user expected gain in relevant information depending on whether it is seen via hits (relevant results retrieved), unseen via misses (relevant results not retrieved), or possibly seen via near-misses (relevant results accessed via navigation). We use these expectations as parameters to formulate evaluation measures for tree retrieval. We then demonstrate how existing task-specific measures, if viewed as tree retrieval, can be formulated, computed and compared using our framework. Finally, we experimentally validate ESR across a range of SDR tasks.},
journal = {Inf. Retr.},
month = dec,
pages = {558–590},
numpages = {33},
keywords = {Redundancy, Evaluation, User navigation, XML retrieval, Effectiveness measures, Tree retrieval, Relevance}
}

@article{10.1007/s10791-012-9184-1,
author = {Brisaboa, Nieves R. and Fari\~{n}a, Antonio and Ladra, Susana and Navarro, Gonzalo},
title = {Implicit Indexing of Natural Language Text by Reorganizing Bytecodes},
year = {2012},
issue_date = {December  2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9184-1},
doi = {10.1007/s10791-012-9184-1},
abstract = {Word-based byte-oriented compression has succeeded on large natural language text databases, by providing competitive compression ratios, fast random access, and direct sequential searching. We show that by just rearranging the target symbols of the compressed text into a tree-shaped structure, and using negligible additional space, we obtain a new implicitly indexed representation of the compressed text, where search times are drastically improved. The occurrences of a word can be listed directly, without any text scanning, and in general any inverted-index-like capability, such as efficient phrase searches, can be emulated without storing any inverted list information. We experimentally show that our proposal performs not only much more efficiently than sequential searches over compressed text, but also than explicit inverted indexes and other types of indexes, when using little extra space. Our representation is especially successful when searching for single words and short phrases.},
journal = {Inf. Retr.},
month = dec,
pages = {527–557},
numpages = {31},
keywords = {Word-based compression, Compressed indexing, Searching compressed text}
}

@article{10.1007/s10791-012-9183-2,
author = {Yu, Qing and Miao, Zhengke and Wu, Gang and Wei, Yimin},
title = {Lumping Algorithms for Computing Google's PageRank and Its Derivative, with Attention to Unreferenced Nodes},
year = {2012},
issue_date = {December  2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9183-2},
doi = {10.1007/s10791-012-9183-2},
abstract = {In this paper, we introduce five type nodes for lumping the Web matrix, and give a unified presentation of some popular lumping methods for PageRank. We show that the PageRank problem can be reduced to solving the PageRank corresponding to the strongly non-dangling and referenced nodes, and the full PageRank vector can be easily derived by some recursion formulations. Our new lumping strategy can reduce the original PageRank problem to a much smaller one, and it is much cheaper than the recursively reordering scheme. Furthermore, we discuss sensitivity of the PageRank vector, and present a lumping algorithm for computing its first order derivative. Numerical experiments show that the new algorithms are favorable when the matrix is large and the damping factor is high.},
journal = {Inf. Retr.},
month = dec,
pages = {503–526},
numpages = {24},
keywords = {Google, PageRank, Unreferenced nodes, Web information retrieval, Dangling nodes}
}

@article{10.1007/s10791-011-9180-x,
author = {Santos, Rodrygo L. and Macdonald, Craig and Ounis, Iadh},
title = {On the Role of Novelty for Search Result Diversification},
year = {2012},
issue_date = {October   2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9180-x},
doi = {10.1007/s10791-011-9180-x},
abstract = {Re-ranking the search results in order to promote novel ones has traditionally been regarded as an intuitive diversification strategy. In this paper, we challenge this common intuition and thoroughly investigate the actual role of novelty for search result diversification, based upon the framework provided by the diversity task of the TREC 2009 and 2010 Web tracks. Our results show that existing diversification approaches based solely on novelty cannot consistently improve over a standard, non-diversified baseline ranking. Moreover, when deployed as an additional component by the current state-of-the-art diversification approaches, our results show that novelty does not bring significant improvements, while adding considerable efficiency overheads. Finally, through a comprehensive analysis with simulated rankings of various quality, we demonstrate that, although inherently limited by the performance of the initial ranking, novelty plays a role at breaking the tie between similarly diverse results.},
journal = {Inf. Retr.},
month = oct,
pages = {478–502},
numpages = {25},
keywords = {Relevance, Web search, Diversity}
}

@article{10.1007/s10791-011-9179-3,
author = {Krestel, Ralf and Fankhauser, Peter},
title = {Reranking Web Search Results for Diversity},
year = {2012},
issue_date = {October   2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9179-3},
doi = {10.1007/s10791-011-9179-3},
abstract = {Search engine results are often biased towards a certain aspect of a query or towards a certain meaning for ambiguous query terms. Diversification of search results offers a way to supply the user with a better balanced result set increasing the probability that a user finds at least one document suiting her information need. In this paper, we present a reranking approach based on minimizing variance of Web search results to improve topic coverage in the top-k results. We investigate two different document representations as the basis for reranking. Smoothed language models and topic models derived by Latent Dirichlet allocation. To evaluate our approach we selected 240 queries from Wikipedia disambiguation pages. This provides us with ambiguous queries together with a community generated balanced representation of their (sub)topics. For these queries we crawled two major commercial search engines. In addition, we present a new evaluation strategy based on Kullback-Leibler divergence and Wikipedia. We evaluate this method using the TREC sub-topic evaluation on the one hand, and manually annotated query results on the other hand. Our results show that minimizing variance in search results by reranking relevant pages significantly improves topic coverage in the top-  k  results with respect to Wikipedia, and gives a good overview of the overall search result. Moreover, latent topic models achieve competitive diversification with significantly less reranking. Finally, our evaluation reveals that our automatic evaluation strategy using Kullback-Leibler divergence correlates well with -nDCG scores used in manual evaluation efforts.},
journal = {Inf. Retr.},
month = oct,
pages = {458–477},
numpages = {20},
keywords = {Wikipedia, Diversity, Topic models, Language models, Web search, Reranking, Diversity evaluation, Variance}
}

@article{10.1007/s10791-011-9178-4,
author = {Zheng, Wei and Wang, Xuanhui and Fang, Hui and Cheng, Hong},
title = {Coverage-Based Search Result Diversification},
year = {2012},
issue_date = {October   2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9178-4},
doi = {10.1007/s10791-011-9178-4},
abstract = {Traditional retrieval models may provide users with less satisfactory search experience because documents are scored independently and the top ranked documents often contain excessively redundant information. Intuitively, it is more desirable to diversify search results so that the top-ranked documents can cover different query subtopics, i.e., different pieces of relevant information. In this paper, we study the problem of search result diversification in an optimization framework whose objective is to maximize a coverage-based diversity function. We first define the diversity score of a set of search results through measuring the coverage of query subtopics in the result set, and then discuss how to use them to derive diversification methods. The key challenge here is how to define an appropriate coverage function given a query and a set of search results. To address this challenge, we propose and systematically study three different strategies to define coverage functions. They are based on summations, loss functions and evaluation measures respectively. Each of these coverage functions leads to a result diversification method. We show that the proposed coverage based diversification methods not only cover several state-of-the-art methods but also allows us to derive new ones. We compare these methods both analytically and empirically. Experiment results on two standard TREC collections show that all the methods are effective for diversification and the new methods can outperform existing ones.},
journal = {Inf. Retr.},
month = oct,
pages = {433–457},
numpages = {25},
keywords = {Diversification, Subtopic, Coverage, Information retrieval}
}

@article{10.1007/s10791-011-9177-5,
author = {Garc\'{\i}a-Cumbreras, M. \'{A}. and Mart\'{\i}nez-Santiago, F. and Ure\~{n}a-L\'{o}pez, L. A.},
title = {Architecture and Evaluation of BRUJA, a Multilingual Question Answering System},
year = {2012},
issue_date = {October   2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9177-5},
doi = {10.1007/s10791-011-9177-5},
abstract = {Given a user question, the goal of a Question Answering (QA) system is to retrieve answers rather than full documents or even best-matching passages, as most Information Retrieval systems currently do. In this paper, we present BRUJA, a QA system for the management of multilingual collections. BRUJ rkstions (English, Spanish and French). The BRUJA architecture is not formed with three monolingual QA systems but instead uses English as Interlingua to make usual QA tasks such as question classifications and answer extractions. In addition, BRUJA uses Cross Language Information Retrieval (CLIR) techniques to retrieve relevant documents from a multilingual collection. On the one hand, we have more documents to find answers from but on the other hand, we are introducing noise into the system because of translations to the Interlingua (English) and the CLIR module. The question is whether the difficulty of managing three languages is worth it or whether a monolingual QA system delivers better results. We report on in-depth experimentation and demonstrate that our multilingual QA system gets better results than its monolingual counterpart whenever it uses good translation resources and, especially, CLIR techniques that are state-of-the-art.},
journal = {Inf. Retr.},
month = oct,
pages = {413–432},
numpages = {20},
keywords = {Cross Language Information Retrieval, Multilingual question answering, Question answering}
}

@article{10.1007/s10791-012-9199-7,
author = {Wang, Fei and Cui, Peng and Sun, Gordon and Chua, Tat-Seng and Yang, Shiqiang},
title = {Guest Editorial: Special Issue on Information Retrieval for Social Media},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9199-7},
doi = {10.1007/s10791-012-9199-7},
journal = {Inf. Retr.},
month = jun,
pages = {179–182},
numpages = {4}
}

@article{10.1007/s10791-012-9196-x,
author = {Zhang, Dan and Si, Luo and Rego, Vernon J.},
title = {Sentiment Detection with Auxiliary Data},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9196-x},
doi = {10.1007/s10791-012-9196-x},
abstract = {As an important application in text mining and social media, sentiment detection has aroused more and more research interests, due to the expanding volume of available online information such as microblogging messages and review comments. Many machine learning methods have been proposed for sentiment detection. As a branch of machine learning, transfer learning is an important technique that tries to transfer knowledge from one domain to another one. When applied to sentiment detection, existing transfer learning methods employ articles with human labeled sentiments from other domains to help the sentiment detection on a target domain. Although most existing transfer learning methods are devoted to handle the data distribution difference between different domains, they only resort to some approximation methods, which may introduce some unnecessary biases. Furthermore, the popular assumption of existing transfer learning techniques on conditional probability is often too strong for practical applications. In this paper, we propose a novel method to model the distribution difference between different domains in sentiment detection by directly modeling the underlying joint distributions for different domains. Some of the important properties of the proposed method, such as the convergence rate and time complexity, are analyzed. The experimental results on the product review dataset and the twitter dataset demonstrate the advantages of the proposed method over the state-of-the-art methods.},
journal = {Inf. Retr.},
month = jun,
pages = {373–390},
numpages = {18},
keywords = {Sentiment detection, Twitter, Social media, Transfer learning, Microblogging}
}

@article{10.1007/s10791-012-9193-0,
author = {Wang, Jingdong and Zhao, Zhe and Zhou, Jiazhen and Wang, Hao and Cui, Bin and Qi, Guojun},
title = {Recommending Flickr Groups with Social Topic Model},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9193-0},
doi = {10.1007/s10791-012-9193-0},
abstract = {The explosion of multimedia content in social media networks raises a great demand of developing tools to facilitate producing, sharing and viewing media content. Flickr groups, self-organized communities with declared common interests, are able to help users to conveniently participate in social media network. In this paper, we address the problem of automatically recommending groups to users. We propose to simultaneously exploit media contents and link structures between users and groups. To this end, we present a probabilistic latent topic model to model them in an integrated framework, expecting to jointly discover the latent interests for users and groups and simultaneously learn the recommendation function. We demonstrate the proposed approach on the dataset crawled from Flickr.com.},
journal = {Inf. Retr.},
month = jun,
pages = {278–295},
numpages = {18},
keywords = {Recommendation, Flickr group, Social topic model}
}

@article{10.1007/s10791-012-9191-2,
author = {Zhou, Dong and Lawless, S\'{e}amus and Wade, Vincent},
title = {Improving Search via Personalized Query Expansion Using Social Media},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9191-2},
doi = {10.1007/s10791-012-9191-2},
abstract = {Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users' social media data.},
journal = {Inf. Retr.},
month = jun,
pages = {218–242},
numpages = {25},
keywords = {Query expansion, Social media, Graph algorithm, Tag-Topic model, Personalization, Web search}
}

@article{10.1007/s10791-012-9190-3,
author = {Tong, Hanghang and Papadimitriou, Spiros and Faloutsos, Christos and Yu, Philip S. and Eliassi-Rad, Tina},
title = {Gateway Finder in Large Graphs: Problem Definitions and Fast Solutions},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9190-3},
doi = {10.1007/s10791-012-9190-3},
abstract = {Given a graph, how to find a small group of `gateways', that is a small subset of nodes that are crucial in connecting the source to the target? For instance, given a social network, who is the best person to introduce you to, say, Chris Ferguson, the poker champion? Or, given a network of people and skills, who is the best person to help you learn about, say, wavelets? We formally formulate this problem in two scenarios: Pair-Gateway and Group-Gateway. For each scenario, we show that it is sub-modular and thus it can be solved near-optimally. We further give fast, scalable algorithms to find such gateways. Extensive experimental evaluations on real data sets demonstrate the effectiveness and efficiency of the proposed methods.},
journal = {Inf. Retr.},
month = jun,
pages = {391–411},
numpages = {21},
keywords = {Sub-modularity, Scalability, Social network, Graph mining, Gateway}
}

@article{10.1007/s10791-012-9189-9,
author = {Magnani, Matteo and Montesi, Danilo and Rossi, Luca},
title = {Conversation Retrieval for Microblogging Sites},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9189-9},
doi = {10.1007/s10791-012-9189-9},
abstract = {In this article we introduce a novel search paradigm for microblogging sites resulting from the intersection of Information Retrieval and Social Network Analysis (SNA). This approach is based on a formal model of on-line conversations and a set of ranking measures including SNA centrality metrics, time-related conversational metrics and other specific features of current microblogging sites. The ranking approach has been compared to other methods and tested on two well known social network sites (Twitter and Friendfeed) showing that the inclusion of SNA metrics in the ranking function and the usage of a model of conversation can improve the results of search tasks.},
journal = {Inf. Retr.},
month = jun,
pages = {354–372},
numpages = {19},
keywords = {Monitoring, Conversation retrieval, Social network analysis metrics, Social media, Social network sites}
}

@article{10.1007/s10791-012-9188-x,
author = {Hao, Tianyong and Agichtein, Eugene},
title = {Finding Similar Questions in Collaborative Question Answering Archives: Toward Bootstrapping-Based Equivalent Pattern Learning},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9188-x},
doi = {10.1007/s10791-012-9188-x},
abstract = {Many questions submitted to Collaborative Question Answering (CQA) sites have similar questions answered before. We propose a precise approach of automatically finding an answer to such questions by automatically identifying "equivalent" questions submitted and answered, in the past. Our method is based on automatically generating equivalent question patterns by grouping together questions that have previously obtained the same answers. The generated patterns are used as seed patterns to match more questions to extract large number of equivalent patterns by a new bootstrapping-based learning method. The resulting patterns can be applied to match a new question to an equivalent one that has already been answered, and thus suggest potential answers automatically. We experimented with this approach over a large collection of more than 200,000 real questions drawn from the Yahoo! Answers archive, automatically acquiring over 16,991 groups of equivalent question patterns. These patterns allow our method to obtain over 57% recall and over 54% precision on suggesting an answer automatically to new questions, significantly improving over baseline methods.},
journal = {Inf. Retr.},
month = jun,
pages = {332–353},
numpages = {22},
keywords = {Collaborative question answering, Bootstrapping, Pattern extension, Equivalent pattern}
}

@article{10.1007/s10791-012-9187-y,
author = {Schedl, Markus},
title = {#nowplaying Madonna: A Large-Scale Evaluation on Estimating Similarities between Music Artists and between Movies from Microblogs},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9187-y},
doi = {10.1007/s10791-012-9187-y},
abstract = {Different term weighting techniques such as  $$TFcdot IDF$$  or  BM25  have been used intensely for manifold text-based information retrieval tasks. Their use for modeling term profiles for named entities and subsequent calculation of similarities between these named entities have been studied to a much smaller extent. The recent trend of microblogging made available massive amounts of information about almost every topic around the world. Therefore, microblogs represent a valuable source for text-based named entity modeling. In this paper, we present a systematic and comprehensive evaluation of different  term weighting measures ,  normalization techniques ,  query schemes ,  index term sets , and  similarity functions  for the task of inferring similarities between named entities, based on data extracted from  microblog posts . We analyze several thousand combinations of choices for the above mentioned dimensions, which influence the similarity calculation process, and we investigate in which way they impact the quality of the similarity estimates. Evaluation is performed using three real-world data sets: two collections of microblogs related to music artists and one related to movies. For the music collections, we present results of  genre classification experiments  using as benchmark genre information from  allmusic.com  . For the movie collection, we present results of  multi-class classification experiments  using as benchmark categories from  IMDb  . We show that microblogs can indeed be exploited to model named entity similarity with remarkable accuracy, provided the correct settings for the analyzed aspects are used. We further compare the results to those obtained when using Web pages as data source.},
journal = {Inf. Retr.},
month = jun,
pages = {183–217},
numpages = {35},
keywords = {Vector space model, Information extraction, Term weighting, Evaluation, Social media mining, Microblog analysis}
}

@article{10.1007/s10791-012-9185-0,
author = {Fleming, Simon and Chalmers, Dan and Wakeman, Ian},
title = {A Deniable and Efficient Question and Answer Service over Ad Hoc Social Networks},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-012-9185-0},
doi = {10.1007/s10791-012-9185-0},
abstract = {When people are connected together over ad hoc social networks, it is possible to ask questions and retrieve answers using the wisdom of the crowd. However, locating a suitable candidate for answering a specific unique question within larger ad hoc groups is non-trivial, especially if we wish to respect the privacy of users by providing deniability. All members of the network wish to source the best possible answers from the network, while at the same time controlling the levels of attention required to generate them by the collective group of individuals and/or the time taken to read all the answers. Conventional expert retrieval approaches rank users for a given query in a centralised indexing process, associating users with material they have previously published. Such an approach is antithetical to privacy, so we have looked to distribute the routing of questions and answers, converting the indexing process into one of building a forwarding table. Starting from the simple operation of flooding the question to everyone, we compare a number of different routing options, where decisions must be made based on past performance and exploitation of the knowledge of our immediate neighbours. We focus on fully decentralised protocols using ant-inspired tactics to route questions towards members of the network who may be able to answer them well. Simultaneously, privacy concerns are acknowledged by allowing both question asking and answering to be plausibly deniable. We have found that via our routing method, it is possible to improve answer quality and also reduce the total amount of user attention required to generate those answers.},
journal = {Inf. Retr.},
month = jun,
pages = {296–331},
numpages = {36},
keywords = {Expert search, Ad hoc social networks, Privacy via plausible deniability, Expert retrieval, Stigmergy, Real time Q&A routing}
}

@article{10.1007/s10791-011-9182-8,
author = {Weerkamp, Wouter and Rijke, Maarten},
title = {Credibility-Inspired Ranking for Blog Post Retrieval},
year = {2012},
issue_date = {June      2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9182-8},
doi = {10.1007/s10791-011-9182-8},
abstract = {Credibility of information refers to its believability or the believability of its sources. We explore the impact of credibility-inspired indicators on the task of blog post retrieval, following the intuition that more credible blog posts are preferred by searchers. Based on a previously introduced credibility framework for blogs, we define several credibility indicators, and divide them into post-level (e.g., spelling, timeliness, document length) and blog-level (e.g., regularity, expertise, comments) indicators. The retrieval task at hand is precision-oriented, and we hypothesize that the use of credibility-inspired indicators will positively impact precision. We propose to use ideas from the credibility framework in a reranking approach to the blog post retrieval problem: We introduce two simple ways of reranking the top n of an initial run. The first approach, Credibility-inspired reranking, simply reranks the top n of a baseline based on the credibility-inspired score. The second approach, Combined reranking, multiplies the credibility-inspired score of the top n results by their retrieval score, and reranks based on this score. Results show that Credibility-inspired reranking leads to larger improvements over the baseline than Combined reranking, but both approaches are capable of improving over an already strong baseline. For Credibility-inspired reranking the best performance is achieved using a combination of all post-level indicators. Combined reranking works best using the post-level indicators combined with comments and pronouns. The blog-level indicators expertise, regularity, and coherence do not contribute positively to the performance, although analysis shows that they can be useful for certain topics. Additional analysis shows that a relative small value of n (15---25) leads to the best results, and that posts that move up the ranking due to the integration of reranking based on credibility-inspired indicators do indeed appear to be more credible than the ones that go down.},
journal = {Inf. Retr.},
month = jun,
pages = {243–277},
numpages = {35},
keywords = {Reranking, Blog post retrieval, Credibility}
}

@article{10.1007/s10791-011-9176-6,
author = {Lee, Yeha and Na, Seung-Hoon and Lee, Jong-Hyeok},
title = {Utilizing Local Evidence for Blog Feed Search},
year = {2012},
issue_date = {April     2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9176-6},
doi = {10.1007/s10791-011-9176-6},
abstract = {Blog feed search aims to identify a blog feed of recurring interest to users on a given topic. A blog feed, the retrieval unit for blog feed search, comprises blog posts of diverse topics. This topical diversity of blog feeds often causes performance deterioration of blog feed search. To alleviate the problem, this paper proposes several approaches based on passage retrieval, widely regarded as effective to handle topical diversity at document level in ad-hoc retrieval. We define the global and local evidence for blog feed search, which correspond to the document-level and passage-level evidence for passage retrieval, respectively, and investigate their influence on blog feed search, in terms of both initial retrieval and pseudo-relevance feedback. For initial retrieval, we propose a retrieval framework to integrate global evidence with local evidence. For pseudo-relevance feedback, we gather feedback information from the local evidence of the top K ranked blog feeds to capture diverse and accurate information related to a given topic. Experimental results show that our approaches using local evidence consistently and significantly outperform traditional ones.},
journal = {Inf. Retr.},
month = apr,
pages = {157–177},
numpages = {21},
keywords = {Blog feed search, Pseudo-relevance feedback, Blog distillation, Passage-based retrieval}
}

@article{10.1007/s10791-011-9175-7,
author = {Krupka, Michal},
title = {On Complexity Reduction of Concept Lattices: Three Counterexamples},
year = {2012},
issue_date = {April     2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9175-7},
doi = {10.1007/s10791-011-9175-7},
abstract = {We discuss the paper by Cheung and Vogel, Inf Retr 8(2), pp 285---299 (2005). We introduce some counterexamples showing that the theoretical results, the paper is based upon, are incorrect.},
journal = {Inf. Retr.},
month = apr,
pages = {151–156},
numpages = {6},
keywords = {Information retrieval, Lattice theory, Complexity reduction, Concept lattice}
}

@article{10.1007/s10791-011-9174-8,
author = {Ganesan, Kavita and Zhai, Chengxiang},
title = {Opinion-Based Entity Ranking},
year = {2012},
issue_date = {April     2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9174-8},
doi = {10.1007/s10791-011-9174-8},
abstract = {The deployment of Web 2.0 technologies has led to rapid growth of various opinions and reviews on the web, such as reviews on products and opinions about people. Such content can be very useful to help people find interesting entities like products, businesses and people based on their individual preferences or tradeoffs. Most existing work on leveraging opinionated content has focused on integrating and summarizing opinions on entities to help users better digest all the opinions. In this paper, we propose a different way of leveraging opinionated content, by directly ranking entities based on a user's preferences. Our idea is to represent each entity with the text of all the reviews of that entity. Given a user's keyword query that expresses the desired features of an entity, we can then rank all the candidate entities based on how well opinions on these entities match the user's preferences. We study several methods for solving this problem, including both standard text retrieval models and some extensions of these models. Experiment results on ranking entities based on opinions in two different domains (hotels and cars) show that the proposed extensions are effective and lead to improvement of ranking accuracy over the standard text retrieval models for this task.},
journal = {Inf. Retr.},
month = apr,
pages = {116–150},
numpages = {35},
keywords = {Vertical search, Preference based entity search, Ad-hoc faceted navigation, Entity oriented search, Product search, Opinion matching}
}

@article{10.1007/s10791-011-9173-9,
author = {Fuhr, Norbert and Lechtenfeld, Marc and Stein, Benno and Gollub, Tim},
title = {The Optimum Clustering Framework: Implementing the Cluster Hypothesis},
year = {2012},
issue_date = {April     2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9173-9},
doi = {10.1007/s10791-011-9173-9},
abstract = {Document clustering offers the potential of supporting users in interactive retrieval, especially when users have problems in specifying their information need precisely. In this paper, we present a theoretic foundation for optimum document clustering. Key idea is to base cluster analysis and evalutation on a set of queries, by defining documents as being similar if they are relevant to the same queries. Three components are essential within our optimum clustering framework, OCF: (1) a set of queries, (2) a probabilistic retrieval method, and (3) a document similarity metric. After introducing an appropriate validity measure, we define optimum clustering with respect to the estimates of the relevance probability for the query-document pairs under consideration. Moreover, we show that well-known clustering methods are implicitly based on the three components, but that they use heuristic design decisions for some of them. We argue that with our framework more targeted research for developing better document clustering methods becomes possible. Experimental results demonstrate the potential of our considerations.},
journal = {Inf. Retr.},
month = apr,
pages = {93–115},
numpages = {23},
keywords = {Document clustering, Probability ranking principle, Cluster metric, Probabilistic retrieval}
}

@article{10.1007/s10791-011-9172-x,
author = {Blanco, Roi and Lioma, Christina},
title = {Graph-Based Term Weighting for Information Retrieval},
year = {2012},
issue_date = {February  2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9172-x},
doi = {10.1007/s10791-011-9172-x},
abstract = {A standard approach to Information Retrieval (IR) is to model text as a bag of words. Alternatively, text can be modelled as a graph, whose vertices represent words, and whose edges represent relations between the words, defined on the basis of any meaningful statistical or linguistic relation. Given such a  text graph , graph theoretic computations can be applied to measure various properties of the graph, and hence of the text. This work explores the usefulness of such graph-based text representations for IR. Specifically, we propose a principled graph-theoretic approach of (1) computing term weights and (2) integrating discourse aspects into retrieval. Given a text graph, whose vertices denote terms linked by co-occurrence and grammatical modification, we use graph ranking computations (e.g. PageRank Page et al. in The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998) to derive weights for each vertex, i.e. term weights, which we use to rank documents against queries. We reason that our graph-based term weights do not necessarily need to be normalised by document length (unlike existing term weights) because they are already scaled by their graph-ranking computation. This is a departure from existing IR ranking functions, and we experimentally show that it performs comparably to a tuned ranking baseline, such as BM25 (Robertson et al. in NIST Special Publication 500-236: TREC-4, 1995). In addition, we integrate into ranking graph properties, such as the average path length, or clustering coefficient, which represent different aspects of the topology of the graph, and by extension of the document represented as a graph. Integrating such properties into ranking allows us to consider issues such as discourse coherence, flow and density during retrieval. We experimentally show that this type of ranking performs comparably to BM25, and can even outperform it, across different TREC (Voorhees and Harman in TREC: Experiment and evaluation in information retrieval, MIT Press, 2005) datasets and evaluation measures.},
journal = {Inf. Retr.},
month = feb,
pages = {54–92},
numpages = {39},
keywords = {Graph theory, Information retrieval, Natural language processing}
}

@article{10.1007/s10791-011-9171-y,
author = {Brahmi, Abderrezak and Ech-Cherif, Ahmed and Benyettou, Abdelkader},
title = {Arabic Texts Analysis for Topic Modeling Evaluation},
year = {2012},
issue_date = {February  2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9171-y},
doi = {10.1007/s10791-011-9171-y},
abstract = {Significant progress has been made in information retrieval covering text semantic indexing and multilingual analysis. However, developments in Arabic information retrieval did not follow the extraordinary growth of Arabic usage in the Web during the ten last years. In the tasks relating to semantic analysis, it is preferable to directly deal with texts in their original language. Studies on topic models, which provide a good way to automatically deal with semantic embedded in texts, are not complete enough to assess the effectiveness of the approach on Arabic texts. This paper investigates several text stemming methods for Arabic topic modeling. A new lemma-based stemmer is described and applied to newspaper articles. The Latent Dirichlet Allocation model is used to extract latent topics from three Arabic real-world corpora. For supervised classification in the topics space, experiments show an improvement when comparing to classification in the full words space or with root-based stemming approach. In addition, topic modeling with lemma-based stemming allows us to discover interesting subjects in the press articles published during the 2007---2009 period.},
journal = {Inf. Retr.},
month = feb,
pages = {33–53},
numpages = {21},
keywords = {Classification, Topic model, Test collections, Arabic stemming, Linguistic analysis}
}

@article{10.1007/s10791-011-9170-z,
author = {Escalante, Hugo Jair and Montes, Manuel and Sucar, Enrique},
title = {Multimodal Indexing Based on Semantic Cohesion for Image Retrieval},
year = {2012},
issue_date = {February  2012},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {15},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9170-z},
doi = {10.1007/s10791-011-9170-z},
abstract = {This paper introduces two novel strategies for representing multimodal images with application to multimedia image retrieval. We consider images that are composed of both text and labels: while text describes the image content at a very high semantic level (e.g., making reference to places, dates or events), labels provide a mid-level description of the image (i.e., in terms of the objects that can be seen in the image). Accordingly, the main assumption of this work is that by combining information from text and labels we can develop very effective retrieval methods. We study standard information fusion techniques for combining both sources of information. However, whereas the performance of such techniques is highly competitive, they cannot capture effectively the content of images. Therefore, we propose two novel representations for multimodal images that attempt to exploit the semantic cohesion among terms from different modalities. Such representations are based on distributional term representations widely used in computational linguistics. Under the considered representations the content of an image is modeled by a distribution of co-occurrences over terms or of occurrences over other images, in such a way that the representation can be considered an expansion of the multimodal terms in the image. We report experimental results using the SAIAPR TC12 benchmark on two sets of topics used in ImageCLEF competitions with manually and automatically generated labels. Experimental results show that the proposed representations outperform significantly both, standard multimodal techniques and unimodal methods. Results on manually assigned labels provide an upper bound in the retrieval performance that can be obtained, whereas results with automatically generated labels are encouraging. The novel representations are able to capture more effectively the content of multimodal images. We emphasize that although we have applied our representations to multimedia image retrieval the same formulation can be adopted for modeling other multimodal documents (e.g., videos).},
journal = {Inf. Retr.},
month = feb,
pages = {1–32},
numpages = {32},
keywords = {Image annotation, Semantic cohesion modeling, Distributional term representations, Multimedia image retrieval}
}

@article{10.5555/2070786.2070818,
author = {Bigot, Anthony and Chrisment, Claude and Dkaki, Taoufiq and Hubert, Gilles and Mothe, Josiane},
title = {Fusing Different Information Retrieval Systems According to Query-Topics: A Study Based on Correlation in Information Retrieval Systems and TREC Topics},
year = {2011},
issue_date = {December  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {6},
issn = {1386-4564},
abstract = {To evaluate Information Retrieval Systems on their effectiveness, evaluation programs such as TREC offer a rigorous methodology as well as benchmark collections. Whatever the evaluation collection used, effectiveness is generally considered globally, averaging the results over a set of information needs. As a result, the variability of system performance is hidden as the similarities and differences from one system to another are averaged. Moreover, the topics on which a given system succeeds or fails are left unknown. In this paper we propose an approach based on data analysis methods (correspondence analysis and clustering) to discover correlations between systems and to find trends in topic/system correlations. We show that it is possible to cluster topics and systems according to system performance on these topics, some system clusters being better on some topics. Finally, we propose a new method to consider complementary systems as based on their performances which can be applied for example in the case of repeated queries. We consider the system profile based on the similarity of the set of TREC topics on which systems achieve similar levels of performance. We show that this method is effective when using the TREC ad hoc collection.},
journal = {Inf. Retr.},
month = dec,
pages = {617–648},
numpages = {32},
keywords = {Clustering, Meta search, System clustering, Dimensionality reduction techniques, Hierarchical clustering, System/query correlation, Local analysis of results, Query clustering, Information retrieval, Correspondence analysis}
}

@article{10.5555/2070786.2070817,
author = {Chapelle, Olivier and Ji, Shihao and Liao, Ciya and Velipasaoglu, Emre and Lai, Larry and Wu, Su-Lin},
title = {Intent-Based Diversification of Web Search Results: Metrics and Algorithms},
year = {2011},
issue_date = {December  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {6},
issn = {1386-4564},
abstract = {We study the problem of web search result diversification in the case where intent based relevance scores are available. A diversified search result will hopefully satisfy the information need of user-L.s who may have different intents. In this context, we first analyze the properties of an intent-based metric, ERR-IA, to measure relevance and diversity altogether. We argue that this is a better metric than some previously proposed intent aware metrics and show that it has a better correlation with abandonment rate. We then propose an algorithm to rerank web search results based on optimizing an objective function corresponding to this metric and evaluate it on shopping related queries.},
journal = {Inf. Retr.},
month = dec,
pages = {572–592},
numpages = {21},
keywords = {Web search, Relevance, Ranking, Diversification}
}

@article{10.1007/s10791-011-9168-6,
author = {Krikon, Eyal and Kurland, Oren},
title = {A Study of the Integration of Passage-, Document-, and Cluster-Based Information for Re-Ranking Search Results},
year = {2011},
issue_date = {December  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9168-6},
doi = {10.1007/s10791-011-9168-6},
abstract = {Cluster-based and passage-based document retrieval paradigms were shown to be effective. While the former are based on utilizing query-related corpus context manifested in clusters of similar documents, the latter address the fact that a document can be relevant even if only a very small part of it contains query-pertaining information. Hence, cluster-based approaches could be viewed as based on "expanding" the document representation, while passage-based approaches can be thought of as utilizing a "contracted" document representation. We present a study of the relative benefits of using each of these two approaches, and of the potential merits of their integration. To that end, we devise two methods that integrate whole-document-based, cluster-based and passage-based information. The methods are applied for the re-ranking task, that is, re-ordering documents in an initially retrieved list so as to improve precision at the very top ranks. Extensive empirical evaluation attests to the potential merits of integrating these information types. Specifically, the resultant performance substantially transcends that of the initial ranking; and, is often better than that of a state-of-the-art pseudo-feedback-based query expansion approach.},
journal = {Inf. Retr.},
month = dec,
pages = {593–616},
numpages = {24},
keywords = {Ad hoc retrieval, Cluster-based language models, Clusters, Passage-based language models, Passages, Re-ranking}
}

@article{10.1007/s10791-011-9166-8,
author = {Seo, Jangwon and Bruce Croft, W. and Smith, David A.},
title = {Online Community Search Using Conversational Structures},
year = {2011},
issue_date = {December  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9166-8},
doi = {10.1007/s10791-011-9166-8},
abstract = {Online communities are valuable information sources where knowledge is accumulated by interactions between people. Search services provided by online community sites such as forums are often, however, quite poor. To address this, we investigate retrieval techniques that exploit the hierarchical thread structures in community sites. Since these structures are sometimes not explicit or accurately annotated, we introduce structure discovery techniques that use a variety of features to model relations between posts. We then make use of thread structures in retrieval experiments with two online forums and one email archive. Our results show that using thread structures that have been accurately annotated can lead to significant improvements in retrieval performance compared to strong baselines.},
journal = {Inf. Retr.},
month = dec,
pages = {547–571},
numpages = {25},
keywords = {Thread structure, Forum search, Online community}
}

@article{10.1007/s10791-011-9165-9,
author = {Weerkamp, Wouter and Balog, Krisztian and Rijke, Maarten},
title = {Blog Feed Search with a Post Index},
year = {2011},
issue_date = {October   2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9165-9},
doi = {10.1007/s10791-011-9165-9},
abstract = {User generated content forms an important domain for mining knowledge. In this paper, we address the task of blog feed search: to find blogs that are principally devoted to a given topic, as opposed to blogs that merely happen to mention the topic in passing. The large number of blogs makes the blogosphere a challenging domain, both in terms of effectiveness and of storage and retrieval efficiency. We examine the effectiveness of an approach to blog feed search that is based on individual posts as indexing units (instead of full blogs). Working in the setting of a probabilistic language modeling approach to information retrieval, we model the blog feed search task by aggregating over a blogger's posts to collect evidence of relevance to the topic and persistence of interest in the topic. This approach achieves state-of-the-art performance in terms of effectiveness. We then introduce a two-stage model where a pre-selection of candidate blogs is followed by a ranking step. The model integrates aggressive pruning techniques as well as very lean representations of the contents of blog posts, resulting in substantial gains in efficiency while maintaining effectiveness at a very competitive level.},
journal = {Inf. Retr.},
month = oct,
pages = {515–545},
numpages = {31},
keywords = {Post-level indexing, Associations, Generative language models, Blog feed search, Efficiency}
}

@article{10.1007/s10791-011-9164-x,
author = {Gao, Bin and Liu, Tie-Yan and Liu, Yuting and Wang, Taifeng and Ma, Zhi-Ming and Li, Hang},
title = {Page Importance Computation Based on Markov Processes},
year = {2011},
issue_date = {October   2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9164-x},
doi = {10.1007/s10791-011-9164-x},
abstract = {This paper is concerned with Markov processes for computing page importance. Page importance is a key factor in Web search. Many algorithms such as PageRank and its variations have been proposed for computing the quantity in different scenarios, using different data sources, and with different assumptions. Then a question arises, as to whether these algorithms can be explained in a unified way, and whether there is a general guideline to design new algorithms for new scenarios. In order to answer these questions, we introduce a General Markov Framework in this paper. Under the framework, a Web Markov Skeleton Process is used to model the random walk conducted by the web surfer on a given graph. Page importance is then defined as the product of two factors: page reachability, the average possibility that the surfer arrives at the page, and page utility, the average value that the page gives to the surfer in a single visit. These two factors can be computed as the stationary probability distribution of the corresponding embedded Markov chain and the mean staying time on each page of the Web Markov Skeleton Process respectively. We show that this general framework can cover many existing algorithms including PageRank, TrustRank, and BrowseRank as its special cases. We also show that the framework can help us design new algorithms to handle more complex problems, by constructing graphs from new data sources, employing new family members of the Web Markov Skeleton Process, and using new methods to estimate these two factors. In particular, we demonstrate the use of the framework with the exploitation of a new process, named Mirror Semi-Markov Process. In the new process, the staying time on a page, as a random variable, is assumed to be dependent on both the current page and its inlink pages. Our experimental results on both the user browsing graph and the mobile web graph validate that the Mirror Semi-Markov Process is more effective than previous models in several tasks, even when there are web spams and when the assumption on preferential attachment does not hold.},
journal = {Inf. Retr.},
month = oct,
pages = {488–514},
numpages = {27},
keywords = {Mirror semi-Markov process, PageRank, BrowseRank, Page importance, Web Markov skeleton process}
}

@article{10.1007/s10791-011-9163-y,
author = {Whissell, John S. and Clarke, Charles L.},
title = {Improving Document Clustering Using Okapi BM25 Feature Weighting},
year = {2011},
issue_date = {October   2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9163-y},
doi = {10.1007/s10791-011-9163-y},
abstract = {We investigate the effect of feature weighting on document clustering, including a novel investigation of Okapi BM25 feature weighting. Using eight document datasets and 17 well-established clustering algorithms we show that the benefit of tf-idf weighting over tf weighting is heavily dependent on both the dataset being clustered and the algorithm used. In addition, binary weighting is shown to be consistently inferior to both tf-idf weighting and tf weighting. We investigate clustering using both BM25 term saturation in isolation and BM25 term saturation with idf, confirming that both are superior to their non-BM25 counterparts under several common clustering quality measures. Finally, we investigate estimation of the k1 BM25 parameter when clustering. Our results indicate that typical values of k1 from other IR tasks are not appropriate for clustering; k1 needs to be higher.},
journal = {Inf. Retr.},
month = oct,
pages = {466–487},
numpages = {22},
keywords = {Okapi BM25, Document clustering, Feature weighting}
}

@article{10.1007/s10791-011-9162-z,
author = {Cormack, Gordon V. and Smucker, Mark D. and Clarke, Charles L.},
title = {Efficient and Effective Spam Filtering and Re-Ranking for Large Web Datasets},
year = {2011},
issue_date = {October   2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-011-9162-z},
doi = {10.1007/s10791-011-9162-z},
abstract = {The TREC 2009 web ad hoc and relevance feedback tasks used a new document collection, the ClueWeb09 dataset, which was crawled from the general web in early 2009. This dataset contains 1 billion web pages, a substantial fraction of which are spam--pages designed to deceive search engines so as to deliver an unwanted payload. We examine the effect of spam on the results of the TREC 2009 web ad hoc and relevance feedback tasks, which used the ClueWeb09 dataset. We show that a simple content-based classifier with minimal training is efficient enough to rank the "spamminess" of every page in the dataset using a standard personal computer in 48 hours, and effective enough to yield significant and substantive improvements in the fixed-cutoff precision (estP10) as well as rank measures (estR-Precision, StatMAP, MAP) of nearly all submitted runs. Moreover, using a set of "honeypot" queries the labeling of training data may be reduced to an entirely automatic process. The results of classical information retrieval methods are particularly enhanced by filtering--from among the worst to among the best.},
journal = {Inf. Retr.},
month = oct,
pages = {441–465},
numpages = {25},
keywords = {Evaluation, Spam, Web search, TREC, Web spam}
}

@article{10.1007/s10791-010-9159-z,
author = {Efron, Miles},
title = {Ay\c{s}e G\"{o}Ker, John Davies (Eds): Information Retrieval: Searching in the 21st Century},
year = {2011},
issue_date = {August    2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9159-z},
doi = {10.1007/s10791-010-9159-z},
journal = {Inf. Retr.},
month = aug,
pages = {438–440},
numpages = {3}
}

@article{10.1007/s10791-010-9150-8,
author = {Meister, Lior and Kurland, Oren and Gelfer Kalmanovich, Inna},
title = {Re-Ranking Search Results Using an Additional Retrieved List},
year = {2011},
issue_date = {August    2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9150-8},
doi = {10.1007/s10791-010-9150-8},
abstract = {We present a novel approach to re-ranking a document list that was retrieved in response to a query so as to improve precision at the very top ranks. The approach is based on utilizing a second list that was retrieved in response to the query by using, for example, a different retrieval method and/or query representation. In contrast to commonly-used methods for fusion of retrieved lists that rely solely on retrieval scores (ranks) of documents, our approach also exploits inter-document-similarities between the lists--a potentially rich source of additional information. Empirical evaluation shows that our methods are effective in re-ranking TREC runs; the resultant performance also favorably compares with that of a highly effective fusion method. Furthermore, we show that our methods can potentially help to tackle a long-standing challenge, namely, integration of document-based and cluster-based retrieved results.},
journal = {Inf. Retr.},
month = aug,
pages = {413–437},
numpages = {25},
keywords = {Inter-document-similarities, Similarity-based fusion, Cluster-based retrieval, Re-ranking, Ad hoc retrieval}
}

@article{10.1007/s10791-010-9147-3,
author = {Baillie, Mark and Carman, Mark and Crestani, Fabio},
title = {A Multi-Collection Latent Topic Model for Federated Search},
year = {2011},
issue_date = {August    2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9147-3},
doi = {10.1007/s10791-010-9147-3},
abstract = {Collection selection is a crucial function, central to the effectiveness and efficiency of a federated information retrieval system. A variety of solutions have been proposed for collection selection adapting proven techniques used in centralised retrieval. This paper defines a new approach to collection selection that models the topical distribution in each collection. We describe an extended version of latent Dirichlet allocation that uses a hierarchical hyperprior to enable the different topical distributions found in each collection to be modelled. Under the model, resources are ranked based on the topical relationship between query and collection. By modelling collections in a low dimensional topic space, we can implicitly smooth their term-based characterisation with appropriate terms from topically related samples, thereby dealing with the problem of missing vocabulary within the samples. An important advantage of adopting this hierarchical model over current approaches is that the model generalises well to unseen documents given small samples of each collection. The latent structure of each collection can therefore be estimated well despite imperfect information for each collection such as sampled documents obtained through query-based sampling. Experiments demonstrate that this new, fully integrated topical model is more robust than current state of the art collection selection algorithms.},
journal = {Inf. Retr.},
month = aug,
pages = {390–412},
numpages = {23},
keywords = {Collection selection, Distributed information retrieval, Topic models, Retrieval}
}

@article{10.1007/s10791-010-9146-4,
author = {Fern\'{a}ndez, Ronald T. and Losada, David E. and Azzopardi, Leif A.},
title = {Extending the Language Modeling Framework for Sentence Retrieval to Include Local Context},
year = {2011},
issue_date = {August    2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9146-4},
doi = {10.1007/s10791-010-9146-4},
abstract = {Employing effective methods of sentence retrieval is essential for many tasks in Information Retrieval, such as summarization, novelty detection and question answering. The best performing sentence retrieval techniques attempt to perform matching directly between the sentences and the query. However, in this paper, we posit that the local context of a sentence can provide crucial additional evidence to further improve sentence retrieval. Using a Language Modeling Framework, we propose a novel reformulation of the sentence retrieval problem that extends previous approaches so that the local context is seamlessly incorporated within the retrieval models. In a series of comprehensive experiments, we show that localized smoothing and the prior importance of a sentence can improve retrieval effectiveness. The proposed models significantly and substantially outperform the state of the art and other competitive sentence retrieval baselines on recall-oriented measures, while remaining competitive on precision-oriented measures. This research demonstrates that local context plays an important role in estimating the relevance of a sentence, and that existing sentence retrieval language models can be extended to utilize this evidence effectively.},
journal = {Inf. Retr.},
month = aug,
pages = {355–389},
numpages = {35},
keywords = {Context, Language models, Sentence retrieval, Information retrieval}
}

@article{10.1007/s10791-010-9161-5,
author = {Sarvabhotla, Kiran and Pingali, Prasad and Varma, Vasudeva},
title = {Sentiment Classification: A Lexical Similarity Based Approach for Extracting Subjectivity in Documents},
year = {2011},
issue_date = {June      2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9161-5},
doi = {10.1007/s10791-010-9161-5},
abstract = {With the growth of social media, document sentiment classification has become an active area of research in this decade. It can be viewed as a special case of topical classification applied only to subjective portions of a document (sources of sentiment). Hence, the key task in document sentiment classification is extracting subjectivity. Existing approaches to extract subjectivity rely heavily on linguistic resources such as sentiment lexicons and complex supervised patterns based on part-of-speech (POS) information. This makes the task of subjective feature extraction complex and resource dependent. In this work, we try to minimize the dependency on linguistic resources in sentiment classification. We propose a simple and statistical methodology called review summary (RSUMM) and use it in combination with well-known feature selection methods to extract subjectivity. Our experimental results on a movie review dataset prove the effectiveness of the proposed methodology.},
journal = {Inf. Retr.},
month = jun,
pages = {337–353},
numpages = {17},
keywords = {Sentiment classification, RSUMM, Social media, Subjectivity, Linguistic resources}
}

@article{10.1007/s10791-010-9160-6,
author = {Diligenti, Michelangelo and Gori, Marco and Maggini, Marco},
title = {A Unified Representation of Web Logs for Mining Applications},
year = {2011},
issue_date = {June      2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9160-6},
doi = {10.1007/s10791-010-9160-6},
abstract = {The collective feedback of the users of an Information Retrieval (IR) system has been shown to provide semantic information that, while hard to extract using standard IR techniques, can be useful in Web mining tasks. In the last few years, several approaches have been proposed to process the logs stored by Internet Service Providers (ISP), Intranet proxies or Web search engines. However, the solutions proposed in the literature only partially represent the information available in the Web logs. In this paper, we propose to use a richer data structure, which is able to preserve most of the information available in the Web logs. This data structure consists of three groups of entities: users, documents and queries, which are connected in a network of relations. Query refinements correspond to separate transitions between the corresponding query nodes in the graph, while users are linked to the queries they have issued and to the documents they have selected. The classical query/document transitions, which connect a query to the documents selected by the users' in the returned result page, are also considered. The resulting data structure is a complete representation of the collective search activity performed by the users of a search engine or of an Intranet. The experimental results show that this more powerful representation can be successfully used in several Web mining tasks like discovering semantically relevant query suggestions and Web page categorization by topic.},
journal = {Inf. Retr.},
month = jun,
pages = {215–236},
numpages = {22},
keywords = {Query recommendations, Query suggestions, User feedback, Query similarity, Text categorization for the web, Web logs}
}

@article{10.1007/s10791-010-9158-0,
author = {Baeza-Yates, Ricardo and Pasi, Gabriella},
title = {Special Issue of The Journal of Information Retrieval on Web Mining for Search},
year = {2011},
issue_date = {June      2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9158-0},
doi = {10.1007/s10791-010-9158-0},
journal = {Inf. Retr.},
month = jun,
pages = {213–214},
numpages = {2}
}

@article{10.1007/s10791-010-9155-3,
author = {Boldi, Paolo and Bonchi, Francesco and Castillo, Carlos and Vigna, Sebastiano},
title = {Query Reformulation Mining: Models, Patterns, and Applications},
year = {2011},
issue_date = {June      2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9155-3},
doi = {10.1007/s10791-010-9155-3},
abstract = {Understanding query reformulation patterns is a key task towards next generation web search engines. If we can do that, then we can build systems able to understand and possibly predict user intent, providing the needed assistance at the right time, and thus helping users locate information more effectively and improving their web-search experience. As a step in this direction, we build a very accurate model for classifying user query reformulations into broad classes (generalization, specialization, error correction or parallel move), achieving 92% accuracy. We then apply the model to automatically label two very large query logs sampled from different geographic areas, and containing a total of approximately 17 million query reformulations. We study the resulting reformulation patterns, matching some results from previous studies performed on smaller manually annotated datasets, and discovering new interesting reformulation patterns, including connections between reformulation types and topical categories. We annotate two large query-flow graphs with reformulation type information, and run several graph-characterization experiments on these graphs, extracting new insights about the relationships between the different query reformulation types. Finally we study query recommendations based on short random walks on the query-flow graphs. Our experiments show that these methods can match in precision, and often improve, recommendations based on query-click graphs, without the need of users' clicks. Our experiments also show that it is important to consider transition-type labels on edges for having recommendations of good quality.},
journal = {Inf. Retr.},
month = jun,
pages = {257–289},
numpages = {33},
keywords = {Query flow graph, Session segmentation, Query log mining, Query recommendation}
}

@article{10.1007/s10791-010-9154-4,
author = {Li, Yuefeng and Algarni, Abdulmohsen and Xu, Yue},
title = {A Pattern Mining Approach for Information Filtering Systems},
year = {2011},
issue_date = {June      2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9154-4},
doi = {10.1007/s10791-010-9154-4},
abstract = {It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well.},
journal = {Inf. Retr.},
month = jun,
pages = {237–256},
numpages = {20},
keywords = {Relevance feedback, Information filtering, Pattern mining}
}

@article{10.1007/s10791-010-9152-6,
author = {Hillard, Dustin and Manavoglu, Eren and Raghavan, Hema and Leggetter, Chris and Cant\'{u}-Paz, Erick and Iyer, Rukmini},
title = {The Sum of Its Parts: Reducing Sparsity in Click Estimation with Query Segments},
year = {2011},
issue_date = {June      2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9152-6},
doi = {10.1007/s10791-010-9152-6},
abstract = {The critical task of predicting clicks on search advertisements is typically addressed by learning from historical click data. When enough history is observed for a given query-ad pair, future clicks can be accurately modeled. However, based on the empirical distribution of queries, sufficient historical information is unavailable for many query-ad pairs. The sparsity of data for new and rare queries makes it difficult to accurately estimate clicks for a significant portion of typical search engine traffic. In this paper we provide analysis to motivate modeling approaches that can reduce the sparsity of the large space of user search queries. We then propose methods to improve click and relevance models for sponsored search by mining click behavior for partial user queries. We aggregate click history for individual query words, as well as for phrases extracted with a CRF model. The new models show significant improvement in clicks and revenue compared to state-of-the-art baselines trained on several months of query logs. Results are reported on live traffic of a commercial search engine, in addition to results from offline evaluation.},
journal = {Inf. Retr.},
month = jun,
pages = {315–336},
numpages = {22},
keywords = {Clicks, Query log mining, Advertising, Relevance}
}

@article{10.1007/s10791-010-9151-7,
author = {Zhou, Bo and Liu, Yiqun and Zhang, Min and Jin, Yijiang and Ma, Shaoping},
title = {Incorporating Web Browsing Activities into Anchor Texts for Web Search},
year = {2011},
issue_date = {June      2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9151-7},
doi = {10.1007/s10791-010-9151-7},
abstract = {Anchor texts complement Web page content and have been used extensively in commercial Web search engines. Existing methods for anchor text weighting rely on the hyperlink information which is created by page content editors. Since anchor texts are created to help user browse the Web, browsing behavior of Web users may also provide useful or complementary information for anchor text weighting. In this paper, we discuss the possibility and effectiveness of incorporating browsing activities of Web users into anchor texts for Web search. We first make an analysis on the effectiveness of anchor texts with browsing activities. And then we propose two new anchor models which incorporate browsing activities. To deal with the data sparseness problem of user-clicked anchor texts, two features of user's browsing behavior are explored and analyzed. Based on these features, a smoothing method for the new anchor models is proposed. Experimental results show that by incorporating browsing activities the new anchor models outperform the state-of-art anchor models which use only the hyperlink information. This study demonstrates the benefits of Web browsing activities to affect anchor text weighting.},
journal = {Inf. Retr.},
month = jun,
pages = {290–314},
numpages = {25},
keywords = {Web browsing behavior, Web search, Document representation, Anchor text}
}

@article{10.1007/s10791-010-9153-5,
author = {Xiaojun, Zhang},
title = {Michael W. Berry and Jacob Kogan (Eds.): Text Mining: Applications and Theory},
year = {2011},
issue_date = {April     2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9153-5},
doi = {10.1007/s10791-010-9153-5},
journal = {Inf. Retr.},
month = apr,
pages = {208–211},
numpages = {4}
}

@article{10.1007/s10791-010-9149-1,
author = {Joho, Hideo},
title = {Diane Kelly: Methods for Evaluating Interactive Information Retrieval Systems with Users},
year = {2011},
issue_date = {April     2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9149-1},
doi = {10.1007/s10791-010-9149-1},
journal = {Inf. Retr.},
month = apr,
pages = {204–207},
numpages = {4}
}

@article{10.1007/s10791-010-9141-9,
author = {Lu, Yue and Mei, Qiaozhu and Zhai, Chengxiang},
title = {Investigating Task Performance of Probabilistic Topic Models: An Empirical Study of PLSA and LDA},
year = {2011},
issue_date = {April     2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9141-9},
doi = {10.1007/s10791-010-9141-9},
abstract = {Probabilistic topic models have recently attracted much attention because of their successful applications in many text mining tasks such as retrieval, summarization, categorization, and clustering. Although many existing studies have reported promising performance of these topic models, none of the work has systematically investigated the task performance of topic models; as a result, some critical questions that may affect the performance of all applications of topic models are mostly unanswered, particularly how to choose between competing models, how multiple local maxima affect task performance, and how to set parameters in topic models. In this paper, we address these questions by conducting a systematic investigation of two representative probabilistic topic models, probabilistic latent semantic analysis (PLSA) and Latent Dirichlet Allocation (LDA), using three representative text mining tasks, including document clustering, text categorization, and ad-hoc retrieval. The analysis of our experimental results provides deeper understanding of topic models and many useful insights about how to optimize the performance of topic models for these typical tasks. The task-based evaluation framework is generalizable to other topic models in the family of either PLSA or LDA.},
journal = {Inf. Retr.},
month = apr,
pages = {178–203},
numpages = {26},
keywords = {LDA, Performance, PLSA, Experimentation, Evaluation, Topic models}
}

@article{10.1007/s10791-010-9139-3,
author = {Fang, Yi and Si, Luo and Mathur, Aditya P.},
title = {Discriminative Probabilistic Models for Expert Search in Heterogeneous Information Sources},
year = {2011},
issue_date = {April     2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9139-3},
doi = {10.1007/s10791-010-9139-3},
abstract = {In many realistic settings of expert finding, the evidence for expertise often comes from heterogeneous knowledge sources. As some sources tend to be more reliable and indicative than the others, different information sources need to receive different weights to reflect their degrees of importance. However, most previous studies in expert finding did not differentiate data sources, which may lead to unsatisfactory performance in the settings where the heterogeneity of data sources is present. In this paper, we investigate how to merge and weight heterogeneous knowledge sources in the context of expert finding. A relevance-based supervised learning framework is presented to learn the combination weights from training data. Beyond just learning a fixed combination strategy for all the queries and experts, we propose a series of discriminative probabilistic models which have increasing capability to associate the combination weights with specific experts and queries. In the last (and also the most sophisticated) proposed model, the combination weights depend on both expert classes and query topics, and these classes/topics are derived from expert and query features. Compared with expert and query independent combination methods, the proposed combination strategy can better adjust to different types of experts and queries. In consequence, the model yields much flexibility of combining data sources when dealing with a broad range of expertise areas and a large variation in experts. To the best of our knowledge, this is the first work that designs discriminative learning models to rank experts. Empirical studies on two real world faculty expertise testbeds demonstrate the effectiveness and robustness of the proposed discriminative learning models.},
journal = {Inf. Retr.},
month = apr,
pages = {158–177},
numpages = {20},
keywords = {Expert finding, Learning to rank, Expert search}
}

@article{10.1007/s10791-010-9138-4,
author = {Manaskasemsak, Bundit and Rungsawang, Arnon and Yamana, Hayato},
title = {Time-Weighted Web Authoritative Ranking},
year = {2011},
issue_date = {April     2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9138-4},
doi = {10.1007/s10791-010-9138-4},
abstract = {We investigate temporal factors in assessing the authoritativeness of web pages. We present three different metrics related to time: age, event, and trend. These metrics measure recentness, special event occurrence, and trend in revisions, respectively. An experimental dataset is created by crawling selected web pages for a period of several months. This data is used to compare page rankings by human users with rankings computed by the standard PageRank algorithm (which does not include temporal factors) and three algorithms that incorporate temporal factors, including the Time-Weighted PageRank (TWPR) algorithm introduced here. Analysis of the rankings shows that all three temporal-aware algorithms produce rankings more like those of human users than does the PageRank algorithm. Of these, the TWPR algorithm produces rankings most similar to human users', indicating that all three temporal factors are relevant in page ranking. In addition, analysis of parameter values used to weight the three temporal factors reveals that age factor has the most impact on page rankings, while trend and event factors have the second and the least impact. Proper weighting of the three factors in TWPR algorithm provides the best ranking results.},
journal = {Inf. Retr.},
month = apr,
pages = {133–157},
numpages = {25},
keywords = {PageRank, Web authoritativeness, Link analysis, Web ranking algorithm, Time-weighted ranking, Search engine}
}

@article{10.1007/s10791-010-9136-6,
author = {Verberne, Suzan and Halteren, Hans and Theijssen, Daphne and Raaijmakers, Stephan and Boves, Lou},
title = {Learning to Rank for Why-Question Answering},
year = {2011},
issue_date = {April     2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9136-6},
doi = {10.1007/s10791-010-9136-6},
abstract = {In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and SVM

                           map
                        ) in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on learning to rank for Information Retrieval.},
journal = {Inf. Retr.},
month = apr,
pages = {107–132},
numpages = {26},
keywords = {Why-questions, Question answering, Learning to rank}
}

@article{10.1007/s10791-010-9157-1,
author = {Carterette, Ben},
title = {An Analysis of NP-Completeness in Novelty and Diversity Ranking},
year = {2011},
issue_date = {February  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9157-1},
doi = {10.1007/s10791-010-9157-1},
abstract = {A useful ability for search engines is to be able to rank objects with novelty and diversity: the top k documents retrieved should cover possible intents of a query with some distribution, or should contain a diverse set of subtopics related to the user's information need, or contain nuggets of information with little redundancy. Evaluation measures have been introduced to measure the effectiveness of systems at this task, but these measures have worst-case NP-hard computation time. The primary consequence of this is that there is no ranking principle akin to the Probability Ranking Principle for document relevance that provides uniform instruction on how to rank documents for novelty and diversity. We use simulation to investigate the practical implications of this for optimization and evaluation of retrieval systems.},
journal = {Inf. Retr.},
month = feb,
pages = {89–106},
numpages = {18},
keywords = {Evaluation, Diversity, Simulation, Novelty, Theory, Test collections}
}

@article{10.1007/s10791-010-9156-2,
author = {Dai, Keshi and Kanoulas, Evangelos and Pavlu, Virgil and Aslam, Javed A.},
title = {Variational Bayes for Modeling Score Distributions},
year = {2011},
issue_date = {February  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9156-2},
doi = {10.1007/s10791-010-9156-2},
abstract = {Empirical modeling of the score distributions associated with retrieved documents is an essential task for many retrieval applications. In this work, we propose modeling the relevant documents' scores by a mixture of Gaussians and the non-relevant scores by a Gamma distribution. Applying Variational Bayes we automatically trade-off the goodness-of-fit with the complexity of the model. We test our model on traditional retrieval functions and actual search engines submitted to TREC. We demonstrate the utility of our model in inferring precision-recall curves. In all experiments our model outperforms the dominant exponential-Gaussian model.},
journal = {Inf. Retr.},
month = feb,
pages = {47–67},
numpages = {21},
keywords = {Recall-precision curves, Score distributions, Gaussian mixtures, Variational inference}
}

@article{10.1007/s10791-010-9145-5,
author = {Arampatzis, Avi and Robertson, Stephen},
title = {Modeling Score Distributions in Information Retrieval},
year = {2011},
issue_date = {February  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9145-5},
doi = {10.1007/s10791-010-9145-5},
abstract = {We review the history of modeling score distributions, focusing on the mixture of normal-exponential by investigating the theoretical as well as the empirical evidence supporting its use. We discuss previously suggested conditions which valid binary mixture models should satisfy, such as the Recall-Fallout Convexity Hypothesis, and formulate two new hypotheses considering the component distributions, individually as well as in pairs, under some limiting conditions of parameter values. From all the mixtures suggested in the past, the current theoretical argument points to the two gamma as the most-likely universal model, with the normal-exponential being a usable approximation. Beyond the theoretical contribution, we provide new experimental evidence showing vector space or geometric models, and BM25, as being `friendly' to the normal-exponential, and that the non-convexity problem that the mixture possesses is practically not severe. Furthermore, we review recent non-binary mixture models, speculate on graded relevance, and consider methods such as logistic regression for score calibration.},
journal = {Inf. Retr.},
month = feb,
pages = {26–46},
numpages = {21},
keywords = {Distributed retrieval, Score distribution, Fusion, Normalization, Filtering}
}

@article{10.1007/s10791-010-9144-6,
author = {Blanke, Tobias and Lalmas, Mounia},
title = {Specificity Aboutness in XML Retrieval},
year = {2011},
issue_date = {February  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9144-6},
doi = {10.1007/s10791-010-9144-6},
abstract = {This paper presents a theoretical methodology to evaluate XML retrieval systems and their filters. Theoretical evaluation is concerned with the formal investigation of qualitative properties of retrieval models. XML retrieval deals with retrieving those document components that specifically answer a query, and filters are a method of delivering the most focussed answers. Our theoretical evaluation critically analyzes how filters achieve this.},
journal = {Inf. Retr.},
month = feb,
pages = {68–88},
numpages = {21},
keywords = {XML retrieval, Information retrieval theory, Theoretical evaluation}
}

@article{10.1007/s10791-010-9143-7,
author = {Clinchant, St\'{e}phane and Gaussier, Eric},
title = {Retrieval Constraints and Word Frequency Distributions a Log-Logistic Model for IR},
year = {2011},
issue_date = {February  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9143-7},
doi = {10.1007/s10791-010-9143-7},
abstract = {We first present in this paper an analytical view of heuristic retrieval constraints which yields simple tests to determine whether a retrieval function satisfies the constraints or not. We then review empirical findings on word frequency distributions and the central role played by burstiness in this context. This leads us to propose a formal definition of burstiness which can be used to characterize probability distributions with respect to this phenomenon. We then introduce the family of information-based IR models which naturally captures heuristic retrieval constraints when the underlying probability distribution is bursty and propose a new IR model within this family, based on the log-logistic distribution. The experiments we conduct on several collections illustrate the good behavior of the log-logistic IR model: It significantly outperforms the Jelinek-Mercer and Dirichlet prior language models on most collections we have used, with both short and long queries and for both the MAP and the precision at 10 documents. It also compares favorably to BM25 and has similar performance to classical DFR models such as InL2 and PL2.},
journal = {Inf. Retr.},
month = feb,
pages = {5–25},
numpages = {21},
keywords = {Log-logistic distribution, Information retrieval theory, Burstiness, Retrieval constraints}
}

@article{10.1007/s10791-010-9142-8,
author = {Azzopardi, Leif and Song, Dawei and Kazai, Gabriella and Robertson, Stephen and R\"{u}ger, Stefan and Shokouhi, Milad and Yilmaz, Emine},
title = {Introduction to Special Issue on the Second International Conference on the Theory of Information Retrieval},
year = {2011},
issue_date = {February  2011},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {14},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9142-8},
doi = {10.1007/s10791-010-9142-8},
journal = {Inf. Retr.},
month = feb,
pages = {1–4},
numpages = {4}
}

@article{10.1007/s10791-010-9148-2,
author = {Bierig, Ralf},
title = {Iris Xie: Interactive Information Retrieval in Digital Environments},
year = {2010},
issue_date = {December

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr