Robust models of mouse movement on dynamic web search results pages

Understanding how users examine result pages across a broad range of information needs is critical for search engine design. Cursor movements can be used to estimate visual attention on search engine results page (SERP) components, including traditional snippets, aggregated results, and advertisements. However, these signals can only be leveraged for SERPs where cursor tracking was enabled, limiting their utility for informing the design of new SERPs. In this work, we develop robust, log-based mouse movement models capable of estimating searcher attention on novel SERP arrangements. These models can help improve SERP design by anticipating searchers' engagement patterns given a proposed arrangement. We demonstrate the efficacy of our method using a large set of mouse-tracking data collected from two independent commercial search engines.

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

[2]  Mike Bennett,et al.  Creating personalized digital human models of perception for visual analytics , 2011, UMAP'11.

[3]  Elizabeth F. Churchill,et al.  Mouse tracking: measuring and predicting users' experience of web-based content , 2012, CHI.

[4]  Ben He.  Probability Ranking Principle , 2009, Encyclopedia of Database Systems.

[5]  Frédo Durand,et al.  Learning to predict where humans look , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

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

[8]  Meredith Ringel Morris,et al.  What do you see when you're surfing?: using eye tracking to predict salient regions of web pages , 2009, CHI.

[9]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

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

[11]  Yuanzhen Li,et al.  Feature congestion: a measure of display clutter , 2005, CHI.

[12]  Eugene Agichtein,et al.  Exploring mouse movements for inferring query intent , 2008, SIGIR '08.

[13]  Mounia Lalmas,et al.  On saliency, affect and focused attention , 2012, CHI.

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

[15]  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.

[16]  Malcolm Slaney,et al.  Using gaze patterns to study and predict reading struggles due to distraction , 2011, CHI Extended Abstracts.

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

[18]  Derrick J. Parkhurst,et al.  Modeling the role of salience in the allocation of overt visual attention , 2002, Vision Research.

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

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

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

[22]  Eugene Agichtein,et al.  Towards predicting web searcher gaze position from mouse movements , 2010, CHI Extended Abstracts.

[23]  Umut Ozertem,et al.  Learning to suggest: a machine learning framework for ranking query suggestions , 2012, SIGIR '12.

[24]  Albrecht Schmidt,et al.  Knowing the User's Every Move – User Activity Tracking for Website Usability Evaluation and Implicit Interaction , 2006 .

[25]  Jane Li,et al.  Good abandonment in mobile and PC internet search , 2009, SIGIR.

[26]  Alexander J. Smola,et al.  Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts , 2013, WWW.

[27]  Srujana Merugu,et al.  The anatomy of a click: modeling user behavior on web information systems , 2010, CIKM '10.

[28]  Andrei Z. Broder,et al.  To swing or not to swing: learning when (not) to advertise , 2008, CIKM '08.

[29]  John U. Farley,et al.  A Stochastic Model of Supermarket Traffic Flow , 1966, Oper. Res..

[30]  Eugene Agichtein,et al.  Ready to buy or just browsing?: detecting web searcher goals from interaction data , 2010, SIGIR.

[31]  Ryen W. White,et al.  User see, user point: gaze and cursor alignment in web search , 2012, CHI.

[32]  Kilian Q. Weinberger,et al.  Feature hashing for large scale multitask learning , 2009, ICML '09.

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

[34]  Ernesto Arroyo,et al.  Usability tool for analysis of web designs using mouse tracks , 2006, CHI Extended Abstracts.

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

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

[37]  Ruth Rosenholtz,et al.  Do predictions of visual perception aid design? , 2011, TAP.

[38]  Alexander J. Smola,et al.  Measurement and modeling of eye-mouse behavior , 2013 .

[39]  S. Robertson.  The probability ranking principle in IR , 1997 .