Playing Your Cards Right: The Effect of Entity Cards on Search Behaviour and Workload

In addition to merging results of different types (e.g.~images, videos, news items) into a ranked list of Web documents, modern search engines have also started displaying entity cards (ECs) on the results page. Entity cards are intended to enhance search experience in several ways: (i) they help searchers navigate diversified results, (ii) provide a summary of relevant content directly on the results page and (iii) support exploratory search by highlighting relevant entities associated with a given user query. We conducted a large-scale crowd-sourced user study, with more than $700$ unique searchers, to investigate the effects of entity cards on search behaviour and perceived workload. We find that the presence of ECs has a strong effect on both the way users interact with search results and their perceived task workload. Furthermore, by manipulating EC properties content, coherence and vertical diversity), we uncover different effects and interactions between card properties on measures of search behaviour and workload. Our study contributes an in-depth analysis of the effects of entity cards on user interaction with modern Web search interfaces.

[1]  Cheri Speier,et al.  The Influence of Query Interface Design on Decision-Making Performance , 2003, MIS Q..

[2]  Robert Villa,et al.  Factors affecting click-through behavior in aggregated search interfaces , 2010, CIKM.

[3]  C. J. Huberty,et al.  Multivariate analysis versus multiple univariate analyses. , 1989 .

[4]  Jaime Arguello,et al.  The effect of cognitive abilities on information search for tasks of varying levels of complexity , 2014, IIiX.

[5]  José J. Cañas,et al.  A neuroergonomic approach to evaluating mental workload in hypermedia interactions , 2011 .

[6]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[7]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

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

[9]  Robert G. Capra,et al.  Factors affecting aggregated search coherence and search behavior , 2013, CIKM.

[10]  Daniel Gayo-Avello,et al.  Survey and evaluation of query intent detection methods , 2009, WSCD '09.

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

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

[13]  Ted Megaw,et al.  The definition and measurement of mental workload , 2005 .

[14]  Robert G. Capra,et al.  The Effects of Vertical Rank and Border on Aggregated Search Coherence and Search Behavior , 2014, CIKM.

[15]  Mark Sanderson,et al.  Ambiguous queries: test collections need more sense , 2008, SIGIR '08.

[16]  Charles L. A. Clarke,et al.  Classifying and Characterizing Query Intent , 2009, ECIR.

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

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

[19]  Yiqun Liu,et al.  Influence of Vertical Result in Web Search Examination , 2015, SIGIR.

[20]  Gavan J. Fitzsimons,et al.  Product Contagion: Changing Consumer Evaluations through Physical Contact with “Disgusting” Products , 2007 .

[21]  Ricardo Baeza-Yates,et al.  Advanced Topics in Information Retrieval , 2011, The Information Retrieval Series.

[22]  Falk Scholer,et al.  Augmenting web search surrogates with images , 2013, CIKM.

[23]  Brian Sternthal,et al.  A Two-Factor Explanation of Assimilation and Contrast Effects , 1993 .

[24]  Jaime Arguello,et al.  Task complexity, vertical display and user interaction in aggregated search , 2012, SIGIR '12.