Towards better measurement of attention and satisfaction in mobile search

Web Search has seen two big changes recently: rapid growth in mobile search traffic, and an increasing trend towards providing answer-like results for relatively simple information needs (e.g., [weather today]). Such results display the answer or relevant information on the search page itself without requiring a user to click. While clicks on organic search results have been used extensively to infer result relevance and search satisfaction, clicks on answer-like results are often rare (or meaningless), making it challenging to evaluate answer quality. Together, these call for better measurement and understanding of search satisfaction on mobile devices. In this paper, we studied whether tracking the browser viewport (visible portion of a web page) on mobile phones could enable accurate measurement of user attention at scale, and provide good measurement of search satisfaction in the absence of clicks. Focusing on answer-like results in web search, we designed a lab study to systematically vary answer presence and relevance (to the user's information need), obtained satisfaction ratings from users, and simultaneously recorded eye gaze and viewport data as users performed search tasks. Using this ground truth, we identified increased scrolling past answer and increased time below answer as clear, measurable signals of user dissatisfaction with answers. While the viewport may contain three to four results at any given time, we found strong correlations between gaze duration and viewport duration on a per result basis, and that the average user attention is focused on the top half of the phone screen, suggesting that we may be able to scalably and reliably identify which specific result the user is looking at, from viewport data alone.

[1]  M A Just,et al.  A theory of reading: from eye fixations to comprehension. , 1980, Psychological review.

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

[3]  Andrei Broder,et al.  A taxonomy of web search , 2002, SIGF.

[4]  Andrew T. Duchowski,et al.  Eye Tracking Methodology: Theory and Practice , 2003, Springer London.

[5]  Thorsten Joachims,et al.  Eye-tracking analysis of user behavior in WWW search , 2004, SIGIR '04.

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

[7]  Päivi Majaranta,et al.  Eye-Tracking Reveals the Personal Styles for Search Result Evaluation , 2005, INTERACT.

[8]  Shumeet Baluja,et al.  A large scale study of wireless search behavior: Google mobile search , 2006, CHI.

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

[10]  Ben Carterette,et al.  Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks , 2007, NIPS.

[11]  Ling Xia,et al.  Eye tracking and online search: Lessons learned and challenges ahead , 2008, J. Assoc. Inf. Sci. Technol..

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

[13]  Susan T. Dumais,et al.  The good, the bad, and the random: an eye-tracking study of ad quality in web search , 2010, SIGIR.

[14]  Susan T. Dumais,et al.  Individual differences in gaze patterns for web search , 2010, IIiX.

[15]  Eugene Agichtein,et al.  ViewSer: enabling large-scale remote user studies of web search examination and interaction , 2011, SIGIR.

[16]  Eugene Agichtein,et al.  Detecting success in mobile search from interaction , 2011, SIGIR '11.

[17]  Jeff Huang Web User Interaction Mining from Touch-Enabled Mobile Devices , 2012 .

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

[19]  Andreas Dengel,et al.  Reading and estimating gaze on smart phones , 2012, ETRA '12.

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

[21]  Eugene Agichtein,et al.  Re-examining search result snippet examination time for relevance estimation , 2012, SIGIR '12.

[22]  Tamás D. Gedeon,et al.  Comparing scanning behaviour in web search on small and large screens , 2012, ADCS.

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

[24]  Eugene Agichtein,et al.  Mining touch interaction data on mobile devices to predict web search result relevance , 2013, SIGIR.