Click Models for Web Search

Abstract With the rapid growth of web search in recent years the problem of modeling its users has started to attract more and more attention of the information retrieval community. This has several motivations. By building a model of user behavior we are essentially developing a better understanding of a user, which ultimately helps us to deliver a better search experience. A model of user behavior can also be used as a predictive device for non-observed items such as document relevance, which makes it useful for improving search result ranking. Finally, in many situations experimenting with real users is just infeasible and hence user simulations based on accurate models play an essential role in understanding the implications of algorithmic changes to search engine results or presentation changes to the search engine result page. In this survey we summarize advances in modeling user click behavior on a web search engine result page. We present simple click models as well as more complex models aimed at...

[1]  R. Fisher,et al.  On the Mathematical Foundations of Theoretical Statistics , 1922 .

[2]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[3]  Cyril W. Cleverdon,et al.  Aslib Cranfield research project - Factors determining the performance of indexing systems; Volume 1, Design; Part 1, Text , 1966 .

[4]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[5]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[6]  Susan T. Dumais,et al.  Optimizing search by showing results in context , 2001, CHI.

[7]  John R. Anderson,et al.  What can a mouse cursor tell us more?: correlation of eye/mouse movements on web browsing , 2001, CHI Extended Abstracts.

[8]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[9]  Susan T. Dumais,et al.  Personalizing Search via Automated Analysis of Interests and Activities , 2005, SIGIR.

[10]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[11]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[12]  Edward Cutrell,et al.  An eye tracking study of the effect of target rank on web search , 2007, CHI.

[13]  Ji-Rong Wen,et al.  WWW 2007 / Track: Search Session: Personalization A Largescale Evaluation and Analysis of Personalized Search Strategies ABSTRACT , 2022 .

[14]  Ryen W. White,et al.  WWW 2007 / Track: Browsers and User Interfaces Session: Personalization Investigating Behavioral Variability in Web Search , 2022 .

[15]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[16]  Matthew Richardson,et al.  Predicting clicks: estimating the click-through rate for new ads , 2007, WWW '07.

[17]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[18]  Benjamin Piwowarski,et al.  Web Search Engine Evaluation Using Clickthrough Data and a User Model , 2007 .

[19]  Tao Qin,et al.  LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval , 2007 .

[20]  Nick Craswell,et al.  An experimental comparison of click position-bias models , 2008, WSDM '08.

[21]  Kerry Rodden,et al.  Eye-mouse coordination patterns on web search results pages , 2008, CHI Extended Abstracts.

[22]  Thorsten Joachims,et al.  Eye tracking and online search: Lessons learned and challenges ahead , 2008 .

[23]  Alistair Moffat,et al.  Rank-biased precision for measurement of retrieval effectiveness , 2008, TOIS.

[24]  Benjamin Piwowarski,et al.  A user browsing model to predict search engine click data from past observations. , 2008, SIGIR '08.

[25]  Mohammad Mahdian,et al.  Externalities in online advertising , 2008, WWW.

[26]  Filip Radlinski,et al.  How does clickthrough data reflect retrieval quality? , 2008, CIKM '08.

[27]  Ron Kohavi,et al.  Controlled experiments on the web: survey and practical guide , 2009, Data Mining and Knowledge Discovery.

[28]  Olivier Chapelle,et al.  A dynamic bayesian network click model for web search ranking , 2009, WWW '09.

[29]  Fernando Diaz,et al.  Sources of evidence for vertical selection , 2009, SIGIR.

[30]  Chao Liu,et al.  Efficient multiple-click models in web search , 2009, WSDM '09.

[31]  J. Shane Culpepper,et al.  Including summaries in system evaluation , 2009, SIGIR.

[32]  Tao Qin,et al.  LETOR: A benchmark collection for research on learning to rank for information retrieval , 2010, Information Retrieval.

[33]  Olivier Chapelle,et al.  Expected reciprocal rank for graded relevance , 2009, CIKM.

[34]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[35]  Chao Liu,et al.  Click chain model in web search , 2009, WWW '09.

[36]  Ciya Liao,et al.  A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine , 2010, WSDM '10.

[37]  Milad Shokouhi,et al.  Expected browsing utility for web search evaluation , 2010, CIKM.

[38]  Erick Cantú-Paz,et al.  Temporal click model for sponsored search , 2010, SIGIR.

[39]  Joaquin Quiñonero Candela,et al.  Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine , 2010, ICML.

[40]  Xiaolong Li,et al.  Inferring search behaviors using partially observable Markov (POM) model , 2010, WSDM '10.

[41]  Yuchen Zhang,et al.  Incorporating post-click behaviors into a click model , 2010, SIGIR.

[42]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[43]  Mark Sanderson,et al.  Test Collection Based Evaluation of Information Retrieval Systems , 2010, Found. Trends Inf. Retr..

[44]  Zhihua Zhang,et al.  Learning click models via probit bayesian inference , 2010, CIKM.

[45]  Laura Hollink,et al.  Search behavior of media professionals at an audiovisual archive: A transaction log analysis , 2010 .

[46]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[47]  Qiang Yang,et al.  A Whole Page Click Model to Better Interpret Search Engine Click Data , 2011, AAAI.

[48]  Ben Carterette,et al.  System effectiveness, user models, and user utility: a conceptual framework for investigation , 2011, SIGIR.

[49]  Katja Hofmann,et al.  A probabilistic method for inferring preferences from clicks , 2011, CIKM '11.

[50]  Yuchen Zhang,et al.  Characterizing search intent diversity into click models , 2011, WWW.

[51]  Kuansan Wang,et al.  Inferring search behaviors using partially observable markov model with duration (POMD) , 2011, WSDM '11.

[52]  Umut Ozertem,et al.  Evaluating new search engine configurations with pre-existing judgments and clicks , 2011, WWW.

[53]  Ryen W. White,et al.  No clicks, no problem: using cursor movements to understand and improve search , 2011, CHI.

[54]  Yuchen Zhang,et al.  User-click modeling for understanding and predicting search-behavior , 2011, KDD.

[55]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[56]  Ryen W. White,et al.  Improving searcher models using mouse cursor activity , 2012, SIGIR '12.

[57]  Tie-Yan Liu,et al.  Relational click prediction for sponsored search , 2012, WSDM '12.

[58]  Charles L. A. Clarke,et al.  Modeling browsing behavior for click analysis in sponsored search , 2012, CIKM '12.

[59]  Steffen Rendle Social Network and Click-through Prediction with Factorization Machines , 2012, KDD 2012.

[60]  Ryen W. White,et al.  Large-scale analysis of individual and task differences in search result page examination strategies , 2012, WSDM '12.

[61]  Yiqun Liu,et al.  Incorporating revisiting behaviors into click models , 2012, WSDM '12.

[62]  Yuchen Zhang,et al.  A noise-aware click model for web search , 2012, WSDM '12.

[63]  Qiang Yang,et al.  Beyond ten blue links: enabling user click modeling in federated web search , 2012, WSDM '12.

[64]  Filip Radlinski,et al.  Large-scale validation and analysis of interleaved search evaluation , 2012, TOIS.

[65]  Qiang Yang,et al.  Personalized click model through collaborative filtering , 2012, WSDM '12.

[66]  Ryen W. White,et al.  Personalized models of search satisfaction , 2013, CIKM.

[67]  Yiqun Liu,et al.  Incorporating vertical results into search click models , 2013, SIGIR.

[68]  M. de Rijke,et al.  Modeling clicks beyond the first result page , 2013, CIKM.

[69]  Fernando Diaz,et al.  Robust models of mouse movement on dynamic web search results pages , 2013, CIKM.

[70]  Katja Hofmann,et al.  Lerot: an online learning to rank framework , 2013, LivingLab '13.

[71]  Yiqun Liu,et al.  Incorporating user preferences into click models , 2013, CIKM.

[72]  Filip Radlinski,et al.  Optimized interleaving for online retrieval evaluation , 2013, WSDM.

[73]  ChengXiang Zhai,et al.  Content-aware click modeling , 2013, WWW '13.

[74]  Katja Hofmann,et al.  Reusing historical interaction data for faster online learning to rank for IR , 2013, DIR.

[75]  M. de Rijke,et al.  Click model-based information retrieval metrics , 2013, SIGIR.

[76]  M. de Rijke,et al.  Using Intent Information to Model User Behavior in Diversified Search , 2013, DIR.

[77]  Katja Hofmann,et al.  Evaluating aggregated search using interleaving , 2013, CIKM.

[78]  Roi Blanco,et al.  Entity Recommendations in Web Search , 2013, SEMWEB.

[79]  Tie-Yan Liu,et al.  Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks , 2014, AAAI.

[80]  Ron Kohavi,et al.  Seven rules of thumb for web site experimenters , 2014, KDD.

[81]  Maarten de Rijke,et al.  Vertical-Aware Click Model-Based Effectiveness Metrics , 2014, CIKM.

[82]  Yiqun Liu,et al.  From Skimming to Reading: A Two-stage Examination Model for Web Search , 2014, CIKM.

[83]  Brian D. Davison,et al.  Exploiting contextual factors for click modeling in sponsored search , 2014, WSDM.

[84]  Floor Sietsma,et al.  Evaluating intuitiveness of vertical-aware click models , 2014, SIGIR.

[85]  Chih-Hung Hsieh,et al.  Towards better measurement of attention and satisfaction in mobile search , 2014, SIGIR.

[86]  Ilya Trofimov,et al.  On peculiarities of positional effects in sponsored search , 2014, SIGIR.

[87]  M. de Rijke,et al.  A Comparative Study of Click Models for Web Search , 2015, CLEF.

[88]  M. de Rijke,et al.  A Comparative Analysis of Interleaving Methods for Aggregated Search , 2015, TOIS.

[89]  Yiqun Liu,et al.  Incorporating Non-sequential Behavior into Click Models , 2015, SIGIR.