Modeling dwell time to predict click-level satisfaction
暂无分享,去创建一个
[1] Filip Radlinski,et al. How does clickthrough data reflect retrieval quality? , 2008, CIKM '08.
[2] Nicholas J. Belkin,et al. Display time as implicit feedback: understanding task effects , 2004, SIGIR '04.
[3] Xiaolong Li,et al. An Overview of Microsoft Web N-gram Corpus and Applications , 2010, NAACL.
[4] Susan T. Dumais,et al. Classification-enhanced ranking , 2010, WWW '10.
[5] W. Bruce Croft,et al. Query performance prediction in web search environments , 2007, SIGIR.
[6] Eugene Agichtein,et al. Beyond dwell time: estimating document relevance from cursor movements and other post-click searcher behavior , 2012, WWW.
[7] Ahmed Hassan Awadallah,et al. Beyond DCG: user behavior as a predictor of a successful search , 2010, WSDM '10.
[8] Ryen W. White,et al. A study on the effects of personalization and task information on implicit feedback performance , 2006, CIKM '06.
[9] Yang Song,et al. A task level metric for measuring web search satisfaction and its application on improving relevance estimation , 2011, CIKM '11.
[10] Susan T. Dumais,et al. Learning user interaction models for predicting web search result preferences , 2006, SIGIR.
[11] Peifeng Yin,et al. Silence is also evidence: interpreting dwell time for recommendation from psychological perspective , 2013, KDD.
[12] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[13] Ryen W. White,et al. Understanding web browsing behaviors through Weibull analysis of dwell time , 2010, SIGIR.
[14] Ryen W. White,et al. Predicting query performance using query, result, and user interaction features , 2010, RIAO.
[15] Ryen W. White,et al. Playing by the rules: mining query associations to predict search performance , 2013, WSDM.
[16] Aristides Gionis,et al. The query-flow graph: model and applications , 2008, CIKM '08.
[17] Eugene Agichtein,et al. Find it if you can: a game for modeling different types of web search success using interaction data , 2011, SIGIR.
[18] Thorsten Joachims,et al. Optimizing search engines using clickthrough data , 2002, KDD.
[19] Ryen W. White,et al. Assessing the scenic route: measuring the value of search trails in web logs , 2010, SIGIR.
[20] W. Bruce Croft,et al. Predicting query performance , 2002, SIGIR '02.
[21] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[22] Kevyn Collins-Thompson,et al. Statistical Estimation of Word Acquisition with Application to Readability Prediction , 2009, EMNLP.
[23] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[24] Qiang Yang,et al. Deep classification in large-scale text hierarchies , 2008, SIGIR '08.
[25] Steve Fox,et al. Evaluating implicit measures to improve web search , 2005, TOIS.
[26] DAVID G. KENDALL,et al. Introduction to Mathematical Statistics , 1947, Nature.
[27] Jaime Teevan,et al. Implicit feedback for inferring user preference: a bibliography , 2003, SIGF.
[28] James Allan,et al. Predicting searcher frustration , 2010, SIGIR.
[29] Jure Leskovec,et al. Web projections: learning from contextual subgraphs of the web , 2007, WWW '07.
[30] Yang Song,et al. A Task Level User Satisfaction Metric and its Application on Improving Relevance Estimation , 2011 .
[31] S. C. Choi,et al. Maximum Likelihood Estimation of the Parameters of the Gamma Distribution and Their Bias , 1969 .
[32] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[33] In-Ho Kang,et al. Query type classification for web document retrieval , 2003, SIGIR.
[34] Nicholas J. Belkin,et al. Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback , 2001, Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.
[35] W. Bruce Croft,et al. Ranking robustness: a novel framework to predict query performance , 2006, CIKM '06.
[36] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[37] Iadh Ounis,et al. Query performance prediction , 2006, Inf. Syst..
[38] J. T. Wulu,et al. Regression analysis of count data , 2002 .
[39] Andreas Dengel,et al. Segment-level display time as implicit feedback: a comparison to eye tracking , 2009, SIGIR.
[40] Scott B. Huffman,et al. How well does result relevance predict session satisfaction? , 2007, SIGIR.
[41] Ahmed Hassan Awadallah,et al. A semi-supervised approach to modeling web search satisfaction , 2012, SIGIR '12.
[42] Susan T. Dumais,et al. Improving Web Search Ranking by Incorporating User Behavior Information , 2019, SIGIR Forum.
[43] Mark Claypool,et al. Implicit interest indicators , 2001, IUI '01.
[44] Kevyn Collins-Thompson,et al. A Language Modeling Approach to Predicting Reading Difficulty , 2004, NAACL.
[45] Nick Craswell,et al. Beyond clicks: query reformulation as a predictor of search satisfaction , 2013, CIKM.
[46] R. Zamar,et al. A multivariate Kolmogorov-Smirnov test of goodness of fit , 1997 .