Detecting Good Abandonment in Mobile Search

Web search queries for which there are no clicks are referred to as abandoned queries and are usually considered as leading to user dissatisfaction. However, there are many cases where a user may not click on any search result page (SERP) but still be satisfied. This scenario is referred to as good abandonment and presents a challenge for most approaches measuring search satisfaction, which are usually based on clicks and dwell time. The problem is exacerbated further on mobile devices where search providers try to increase the likelihood of users being satisfied directly by the SERP. This paper proposes a solution to this problem using gesture interactions, such as reading times and touch actions, as signals for differentiating between good and bad abandonment. These signals go beyond clicks and characterize user behavior in cases where clicks are not needed to achieve satisfaction. We study different good abandonment scenarios and investigate the different elements on a SERP that may lead to good abandonment. We also present an analysis of the correlation between user gesture features and satisfaction. Finally, we use this analysis to build models to automatically identify good abandonment in mobile search achieving an accuracy of 75%, which is significantly better than considering query and session signals alone. Our findings have implications for the study and application of user satisfaction in search systems.

[1]  J. Fleiss Measuring nominal scale agreement among many raters. , 1971 .

[2]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  Wei Fan,et al.  On the Optimality of Probability Estimation by Random Decision Trees , 2004, AAAI.

[5]  Steve Fox,et al.  Evaluating implicit measures to improve web search , 2005, TOIS.

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

[7]  Diane Kelly,et al.  Methods for Evaluating Interactive Information Retrieval Systems with Users , 2009, Found. Trends Inf. Retr..

[8]  Ya Xu,et al.  Computers and iphones and mobile phones, oh my!: a logs-based comparison of search users on different devices , 2009, WWW '09.

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

[10]  Sofia Stamou,et al.  Interpreting User Inactivity on Search Results , 2010, ECIR.

[11]  Ahmed Hassan Awadallah,et al.  Beyond DCG: user behavior as a predictor of a successful search , 2010, WSDM '10.

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

[13]  Lydia B. Chilton,et al.  Addressing people's information needs directly in a web search result page , 2011, WWW.

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

[15]  Aleksandr Chuklin,et al.  Good abandonments in factoid queries , 2012, WWW.

[16]  Michael S. Bernstein,et al.  Direct answers for search queries in the long tail , 2012, CHI.

[17]  Yang Song,et al.  Evaluating the effectiveness of search task trails , 2012, WWW.

[18]  Eugene Agichtein,et al.  Predicting web search success with fine-grained interaction data , 2012, CIKM.

[19]  Ahmed Hassan Awadallah A semi-supervised approach to modeling web search satisfaction , 2012 .

[20]  Aleksandr Chuklin,et al.  Potential good abandonment prediction , 2012, WWW.

[21]  Ryen W. White,et al.  Leaving so soon?: understanding and predicting web search abandonment rationales , 2012, CIKM.

[22]  Eugene Agichtein,et al.  Beyond dwell time: estimating document relevance from cursor movements and other post-click searcher behavior , 2012, WWW.

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

[24]  Ryen W. White,et al.  Playing by the rules: mining query associations to predict search performance , 2013, WSDM.

[25]  Aaron Smith,et al.  Cell internet use 2013 , 2013 .

[26]  Nick Craswell,et al.  Beyond clicks: query reformulation as a predictor of search satisfaction , 2013, CIKM.

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

[28]  Yang Song,et al.  Context-aware web search abandonment prediction , 2014, SIGIR.

[29]  Ryen W. White,et al.  Comparing client and server dwell time estimates for click-level satisfaction prediction , 2014, SIGIR.

[30]  Ryen W. White,et al.  Modeling dwell time to predict click-level satisfaction , 2014, WSDM.

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

[32]  Yiqun Liu,et al.  Different Users, Different Opinions: Predicting Search Satisfaction with Mouse Movement Information , 2015, SIGIR.

[33]  Imed Zitouni,et al.  Automatic Online Evaluation of Intelligent Assistants , 2015, WWW.

[34]  Meng Wang,et al.  Does Vertical Bring more Satisfaction?: Predicting Search Satisfaction in a Heterogeneous Environment , 2015, CIKM.

[35]  Ryen W. White,et al.  Questions vs. Queries in Informational Search Tasks , 2015, WWW.

[36]  Ryen W. White,et al.  Understanding and Predicting Graded Search Satisfaction , 2015, WSDM.