Dynamic Information Retrieval Modeling
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[1] Nicholas J. Belkin,et al. A case for interaction: a study of interactive information retrieval behavior and effectiveness , 1996, CHI.
[2] Walid Magdy,et al. PRES: a score metric for evaluating recall-oriented information retrieval applications , 2010, SIGIR.
[3] Sean M. McNee,et al. Getting to know you: learning new user preferences in recommender systems , 2002, IUI '02.
[4] Wei Chu,et al. Online learning for recency search ranking using real-time user feedback , 2010, CIKM '10.
[5] Ingrid Renz,et al. Adaptive information filtering: detecting changes in text streams , 1999, CIKM '99.
[6] Hwanjo Yu,et al. SVM selective sampling for ranking with application to data retrieval , 2005, KDD '05.
[7] Zhen Shu,et al. Web document ranking via active learning and kernel principal component analysis , 2015 .
[8] Masatoshi Yoshikawa,et al. Adaptive web search based on user profile constructed without any effort from users , 2004, WWW '04.
[9] Daniel S. Hirschberg,et al. Algorithms for the Longest Common Subsequence Problem , 1977, JACM.
[10] Grace Hui Yang,et al. A POMDP model for content-free document re-ranking , 2014, SIGIR.
[11] Thorsten Joachims,et al. Interactively optimizing information retrieval systems as a dueling bandits problem , 2009, ICML '09.
[12] Milad Shokouhi,et al. Query Suggestion and Data Fusion in Contextual Disambiguation , 2015, WWW.
[13] Yang Song,et al. Query suggestion by constructing term-transition graphs , 2012, WSDM '12.
[14] Thorsten Joachims,et al. The K-armed Dueling Bandits Problem , 2012, COLT.
[15] Wenbin Cai,et al. Active Learning for Web Search Ranking via Noise Injection , 2015, TWEB.
[16] Ian Ruthven,et al. Re-examining the potential effectiveness of interactive query expansion , 2003, SIGIR.
[17] Oren Etzioni,et al. Grouper: A Dynamic Clustering Interface to Web Search Results , 1999, Comput. Networks.
[18] Yi Zhang. Using bayesian priors to combine classifiers for adaptive filtering , 2004, SIGIR '04.
[19] Lorenzo Bruzzone,et al. A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[20] Kuansan Wang,et al. Inferring search behaviors using partially observable markov model with duration (POMD) , 2011, WSDM '11.
[21] John Riedl,et al. Learning preferences of new users in recommender systems: an information theoretic approach , 2008, SKDD.
[22] Minyi Guo,et al. Location-Aware Information Retrieval for Mobile Computing , 2004, EUC.
[23] Ryen W. White,et al. Modeling and analysis of cross-session search tasks , 2011, SIGIR.
[24] Katja Hofmann,et al. Balancing Exploration and Exploitation in Learning to Rank Online , 2011, ECIR.
[25] Olivier Chapelle,et al. Expected reciprocal rank for graded relevance , 2009, CIKM.
[26] Charles L. A. Clarke,et al. The influence of caption features on clickthrough patterns in web search , 2007, SIGIR.
[27] Shuang-Hong Yang,et al. Functional matrix factorizations for cold-start recommendation , 2011, SIGIR.
[28] Jun Wang,et al. Sequential selection of correlated ads by POMDPs , 2012, CIKM.
[29] Grace Hui Yang,et al. Designing States, Actions, and Rewards for Using POMDP in Session Search , 2015, ECIR.
[30] M. de Rijke,et al. Building simulated queries for known-item topics: an analysis using six european languages , 2007, SIGIR.
[31] James Allan,et al. Task-aware query recommendation , 2013, SIGIR.
[32] Tetsuya Sakai,et al. Evaluating diversified search results using per-intent graded relevance , 2011, SIGIR.
[33] Amanda Spink,et al. Use of query reformulation and relevance feedback by Excite users , 2000, Internet Res..
[34] Mika Käki,et al. Controlling the complexity in comparing search user interfaces via user studies , 2008, Information Processing & Management.
[35] Wei Li,et al. Exploitation and exploration in a performance based contextual advertising system , 2010, KDD.
[36] Thorsten Joachims,et al. Optimizing search engines using clickthrough data , 2002, KDD.
[37] Jun Wang,et al. Unifying user-based and item-based collaborative filtering approaches by similarity fusion , 2006, SIGIR.
[38] Gary Marchionini,et al. Report on ACM SIGIR 2006 workshop on evaluating exploratory search systems , 2006, SIGF.
[39] Gerard Salton,et al. A vector space model for automatic indexing , 1975, CACM.
[40] Filip Radlinski,et al. Optimized interleaving for online retrieval evaluation , 2013, WSDM.
[41] Norbert Fuhr,et al. A probability ranking principle for interactive information retrieval , 2008, Information Retrieval.
[42] Ryen W. White,et al. Modeling dwell time to predict click-level satisfaction , 2014, WSDM.
[43] W. Bruce Croft,et al. Query reformulation using anchor text , 2010, WSDM '10.
[44] Nish Parikh,et al. Scalable and near real-time burst detection from eCommerce queries , 2008, KDD.
[45] Gabriella Kazai,et al. Structural relevance: a common basis for the evaluation of structured document retrieval , 2008, CIKM '08.
[46] Susan T. Dumais,et al. The web changes everything: understanding the dynamics of web content , 2009, WSDM '09.
[47] Mounia Lalmas,et al. A survey on the use of relevance feedback for information access systems , 2003, The Knowledge Engineering Review.
[48] Lois M. L. Delcambre,et al. Discounted Cumulated Gain Based Evaluation of Multiple-Query IR Sessions , 2008, ECIR.
[49] Wei Chu,et al. A contextual-bandit approach to personalized news article recommendation , 2010, WWW '10.
[50] Jure Leskovec,et al. Meme-tracking and the dynamics of the news cycle , 2009, KDD.
[51] James Allan,et al. Predicting searcher frustration , 2010, SIGIR.
[52] Yi Zhang,et al. Interactive retrieval based on faceted feedback , 2010, SIGIR '10.
[53] Xuehua Shen,et al. Context-sensitive information retrieval using implicit feedback , 2005, SIGIR '05.
[54] Charles L. A. Clarke,et al. Novelty and diversity in information retrieval evaluation , 2008, SIGIR '08.
[55] Jun Wang,et al. A term-based methodology for query reformulation understanding , 2015, Information Retrieval Journal.
[56] Vidit Jain,et al. Learning to re-rank: query-dependent image re-ranking using click data , 2011, WWW.
[57] Alistair Moffat,et al. Rank-biased precision for measurement of retrieval effectiveness , 2008, TOIS.
[58] Hsinchun Chen,et al. Summary in context: Searching versus browsing , 2006, TOIS.
[59] Jun Wang,et al. Interactive exploratory search for multi page search results , 2013, WWW.
[60] Grace Hui Yang,et al. Learning to Reinforce Search Effectiveness , 2015, ICTIR.
[61] Grace Hui Yang,et al. The water filling model and the cube test: multi-dimensional evaluation for professional search , 2013, CIKM.
[62] Stephen E. Robertson,et al. Selecting good expansion terms for pseudo-relevance feedback , 2008, SIGIR '08.
[63] Yiming Yang,et al. Personalized active learning for collaborative filtering , 2008, SIGIR '08.
[64] R. Agrawal. Sample mean based index policies by O(log n) regret for the multi-armed bandit problem , 1995, Advances in Applied Probability.
[65] A. A. Markov,et al. An Example of Statistical Investigation of the Text Eugene Onegin Concerning the Connection of Samples in Chains , 2006, Science in Context.
[66] Thomas Hofmann,et al. Unifying collaborative and content-based filtering , 2004, ICML.
[67] Paul N. Bennett,et al. Toward whole-session relevance: exploring intrinsic diversity in web search , 2013, SIGIR.
[68] Filip Radlinski,et al. Large-scale validation and analysis of interleaved search evaluation , 2012, TOIS.
[69] Wei Chu,et al. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms , 2010, WSDM '11.
[70] Jaime G. Carbonell,et al. Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve , 2009, ECIR.
[71] M. de Rijke,et al. Multileave Gradient Descent for Fast Online Learning to Rank , 2016, WSDM.
[72] Young-Woo Seo,et al. A reinforcement learning agent for personalized information filtering , 2000, IUI '00.
[73] Jingjing Liu,et al. Personalizing information retrieval for multi‐session tasks: Examining the roles of task stage, task type, and topic knowledge on the interpretation of dwell time as an indicator of document usefulness , 2015, J. Assoc. Inf. Sci. Technol..
[74] Andrei Broder,et al. A taxonomy of web search , 2002, SIGF.
[75] Nick Craswell,et al. An experimental comparison of click position-bias models , 2008, WSDM '08.
[76] Paul Over,et al. TREC-8 interactive track , 1999, SIGF.
[77] Robert D. Kleinberg,et al. Regret bounds for sleeping experts and bandits , 2010, Machine Learning.
[78] John D. Lafferty,et al. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval , 2003, SIGIR.
[79] Ingmar Weber,et al. Type less, find more: fast autocompletion search with a succinct index , 2006, SIGIR.
[80] ChengXiang Zhai,et al. A learning approach to optimizing exploration–exploitation tradeoff in relevance feedback , 2012, Information Retrieval.
[81] Katja Hofmann,et al. Fast and reliable online learning to rank for information retrieval , 2013, SIGIR Forum.
[82] William S. Cooper,et al. On selecting a measure of retrieval effectiveness , 1973, J. Am. Soc. Inf. Sci..
[83] Tie-Yan Liu,et al. Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.
[84] Paul Over,et al. Comparing interactive information retrieval systems across sites: the TREC-6 interactive track matrix experiment , 1998, SIGIR '98.
[85] Jun Wang,et al. Dynamic Information Retrieval: Theoretical Framework and Application , 2015, ICTIR.
[86] Grace Hui Yang,et al. Win-win search: dual-agent stochastic game in session search , 2014, SIGIR.
[87] Nicholas J. Belkin,et al. Relationships between categories of relevance criteria and stage in task completion , 2007, Inf. Process. Manag..
[88] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[89] Jun Wang,et al. Using control theory for stable and efficient recommender systems , 2012, WWW.
[90] Charles L. A. Clarke,et al. Time-based calibration of effectiveness measures , 2012, SIGIR '12.
[91] Filip Radlinski,et al. Learning diverse rankings with multi-armed bandits , 2008, ICML '08.
[92] Steve Fox,et al. Evaluating implicit measures to improve web search , 2005, TOIS.
[93] Jun Wang,et al. Dynamical information retrieval modelling: a portfolio-armed bandit machine approach , 2012, WWW.
[94] Milad Shokouhi,et al. Learning to personalize query auto-completion , 2013, SIGIR.
[95] Vassilis Plachouras,et al. Online learning from click data for sponsored search , 2008, WWW.
[96] Stephen E. Robertson,et al. A new interpretation of average precision , 2008, SIGIR '08.
[97] Matthew Lease,et al. Active learning to maximize accuracy vs. effort in interactive information retrieval , 2011, SIGIR.
[98] James Allan,et al. Incremental relevance feedback for information filtering , 1996, SIGIR '96.
[99] George Karypis,et al. Item-based top-N recommendation algorithms , 2004, TOIS.
[100] Susan T. Dumais,et al. Personalizing atypical web search sessions , 2013, WSDM.
[101] Wei Chu,et al. Learning to extract cross-session search tasks , 2013, WWW.
[102] Robert G. Capra,et al. NSF workshop on task-based information search systems , 2013, SIGIR Forum.
[103] Marc Najork,et al. A large‐scale study of the evolution of Web pages , 2003, WWW '03.
[104] Yi Zhang,et al. Novelty and redundancy detection in adaptive filtering , 2002, SIGIR '02.
[105] Francesco Ricci,et al. Learning and adaptivity in interactive recommender systems , 2007, ICEC.
[106] Greg Linden,et al. Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .
[107] Grace Hui Yang,et al. Query change as relevance feedback in session search , 2013, SIGIR.
[108] Yehuda Koren,et al. Adaptive bootstrapping of recommender systems using decision trees , 2011, WSDM '11.
[109] Hyung Jun Ahn,et al. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem , 2008, Inf. Sci..
[110] Nicolò Cesa-Bianchi,et al. Gambling in a rigged casino: The adversarial multi-armed bandit problem , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.
[111] Ryen W. White,et al. Leaving so soon?: understanding and predicting web search abandonment rationales , 2012, CIKM.
[112] David R. Karger,et al. Less is More Probabilistic Models for Retrieving Fewer Relevant Documents , 2006 .
[113] Leslie Pack Kaelbling,et al. Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..
[114] David M. Pennock,et al. Categories and Subject Descriptors , 2001 .
[115] Chao Liu,et al. Click chain model in web search , 2009, WWW '09.
[116] Jaime Teevan,et al. Implicit feedback for inferring user preference: a bibliography , 2003, SIGF.
[117] C. Lee Giles,et al. Discovering Relevant Scientific Literature on the Web , 2000, IEEE Intell. Syst..
[118] Andrew Trotman,et al. Comparative analysis of clicks and judgments for IR evaluation , 2009, WSCD '09.
[119] Susan T. Dumais,et al. To personalize or not to personalize: modeling queries with variation in user intent , 2008, SIGIR '08.
[120] Grace Hui Yang,et al. Session Search by Direct Policy Learning , 2015, ICTIR.
[121] Leif Azzopardi,et al. The economics in interactive information retrieval , 2011, SIGIR.
[122] J. J. Rocchio,et al. Relevance feedback in information retrieval , 1971 .
[123] Leif Azzopardi,et al. Modelling interaction with economic models of search , 2014, SIGIR.
[124] Ryen W. White,et al. Exploratory Search: Beyond the Query-Response Paradigm , 2009, Exploratory Search: Beyond the Query-Response Paradigm.
[125] Tetsuya Sakai,et al. Summaries, ranked retrieval and sessions: a unified framework for information access evaluation , 2013, SIGIR.
[126] Amanda Spink,et al. Multitasking during Web search sessions , 2006, Inf. Process. Manag..
[127] Tie-Yan Liu. Learning to Rank for Information Retrieval , 2009, Found. Trends Inf. Retr..
[128] Jude W. Shavlik,et al. Learning users' interests by unobtrusively observing their normal behavior , 2000, IUI '00.
[129] Marc-Allen Cartright,et al. Intentions and attention in exploratory health search , 2011, SIGIR.
[130] Enhong Chen,et al. Context-aware query suggestion by mining click-through and session data , 2008, KDD.
[131] Burr Settles,et al. Active Learning Literature Survey , 2009 .
[132] Grace Hui Yang,et al. Utilizing query change for session search , 2013, SIGIR.
[133] Gilad Mishne,et al. Towards recency ranking in web search , 2010, WSDM '10.
[134] Jun Wang,et al. Dynamic Information Retrieval Modeling , 2015, Synthesis Lectures on Information Concepts, Retrieval, and Services.
[135] Wenbin Cai,et al. Active learning for ranking with sample density , 2015, Information Retrieval Journal.
[136] Ben Carterette,et al. Evaluating multi-query sessions , 2011, SIGIR.
[137] Jaana Kekäläinen,et al. Cumulated gain-based evaluation of IR techniques , 2002, TOIS.
[138] Ben Carterette,et al. Simulating simple user behavior for system effectiveness evaluation , 2011, CIKM '11.
[139] Susan T. Dumais,et al. Understanding temporal query dynamics , 2011, WSDM '11.
[140] Daqing He,et al. Searching, browsing, and clicking in a search session: changes in user behavior by task and over time , 2014, SIGIR.
[141] Filip Radlinski,et al. Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search , 2007, TOIS.
[142] Edward J. Sondik,et al. The Optimal Control of Partially Observable Markov Processes over the Infinite Horizon: Discounted Costs , 1978, Oper. Res..
[143] Yang Song,et al. Optimal rare query suggestion with implicit user feedback , 2010, WWW '10.
[144] Ram Akella,et al. Active relevance feedback for difficult queries , 2008, CIKM '08.
[145] Stephen E. Robertson,et al. Threshold setting in adaptive filtering , 2000, J. Documentation.
[146] Douglas B. Terry,et al. Using collaborative filtering to weave an information tapestry , 1992, CACM.
[147] Deepak Agarwal,et al. Click shaping to optimize multiple objectives , 2011, KDD.
[148] Kevin D. Glazebrook,et al. Multi-Armed Bandit Allocation Indices: Gittins/Multi-Armed Bandit Allocation Indices , 2011 .
[149] Susan T. Dumais,et al. Personalized information delivery: an analysis of information filtering methods , 1992, CACM.
[150] H. Vincent Poor,et al. Cognitive Medium Access: Exploration, Exploitation, and Competition , 2007, IEEE Transactions on Mobile Computing.
[151] Thomas W. Malone,et al. Intelligent Information Sharing Systems , 1986 .
[152] Andrew Turpin,et al. Do batch and user evaluations give the same results? , 2000, SIGIR '00.
[153] Jun Wang,et al. Portfolio theory of information retrieval , 2009, SIGIR.
[154] Matthias Hemmje,et al. A 3D Based User Interface for Information Retrieval Systems , 1993, Workshop on Database Issues for Data Visualization.
[155] Ellen M. Voorhees,et al. The TREC robust retrieval track , 2005, SIGF.
[156] Ryen W. White,et al. A study of factors affecting the utility of implicit relevance feedback , 2005, SIGIR '05.
[157] S. Robertson. The probability ranking principle in IR , 1997 .
[158] Thorsten Joachims,et al. Dynamic ranked retrieval , 2011, WSDM '11.
[159] J. Pearl. Causal inference in statistics: An overview , 2009 .
[160] Peter Auer,et al. The Nonstochastic Multiarmed Bandit Problem , 2002, SIAM J. Comput..
[161] Yiming Yang,et al. Modeling Expected Utility of Multi-session Information Distillation , 2009, ICTIR.
[162] Falk Scholer,et al. The effect of threshold priming and need for cognition on relevance calibration and assessment , 2013, SIGIR.
[163] Lydia B. Chilton,et al. Addressing people's information needs directly in a web search result page , 2011, WWW.
[164] W. Bruce Croft,et al. LDA-based document models for ad-hoc retrieval , 2006, SIGIR.
[165] ChengXiang Zhai,et al. Active feedback in ad hoc information retrieval , 2005, SIGIR '05.
[166] Chao Liu,et al. Efficient multiple-click models in web search , 2009, WSDM '09.
[167] Filip Radlinski,et al. Relevance and Effort: An Analysis of Document Utility , 2014, CIKM.
[168] Deepayan Chakrabarti,et al. Multi-armed bandit problems with dependent arms , 2007, ICML '07.
[169] Thorsten Joachims,et al. Structured learning of two-level dynamic rankings , 2011, CIKM '11.
[170] H. Robbins,et al. Asymptotically efficient adaptive allocation rules , 1985 .
[171] William A. Gale,et al. A sequential algorithm for training text classifiers , 1994, SIGIR '94.
[172] Eric Brill,et al. Improving web search ranking by incorporating user behavior information , 2006, SIGIR.
[173] Olivier Chapelle,et al. A dynamic bayesian network click model for web search ranking , 2009, WWW '09.
[174] R. Bellman. A Markovian Decision Process , 1957 .
[175] Amanda Spink,et al. How are we searching the World Wide Web? A comparison of nine search engine transaction logs , 2006, Inf. Process. Manag..
[176] Eli Upfal,et al. Multi-Armed Bandits in Metric Spaces ∗ , 2008 .
[177] Ricardo A. Baeza-Yates,et al. Query Recommendation Using Query Logs in Search Engines , 2004, EDBT Workshops.
[178] Yoichi Shinoda,et al. Information filtering based on user behavior analysis and best match text retrieval , 1994, SIGIR '94.
[179] Hwee Tou Ng,et al. Bayesian online classifiers for text classification and filtering , 2002, SIGIR '02.
[180] Jure Leskovec,et al. Patterns of temporal variation in online media , 2011, WSDM '11.
[181] Jaime Teevan,et al. Understanding how people interact with web search results that change in real-time using implicit feedback , 2013, CIKM.
[182] Nicholas J. Belkin,et al. Information filtering and information retrieval: two sides of the same coin? , 1992, CACM.
[183] ChengXiang Zhai,et al. Implicit user modeling for personalized search , 2005, CIKM '05.