Evaluating user search trails in exploratory search tasks

Exploratory search is characterized by a users uncertainty towards a complex information seeking task. A user conducting such a search in an information retrieval (IR) system may need help and recommendations that are beyond mere query suggestions. In this paper we propose a new method for recommending search trails to struggling users. We first use a search process prediction model from the literature to predict whether a user is likely to under-perform in an exploratory search task, and given that case, recommend a search trail based on other users search behaviors in a similar context. We then present a method to evaluate the effectiveness of these recommendations that involves two different evaluation criteria. First, we use Open Directory Project (ODP)-based categorization of user-traversed Web pages to evaluate each users information coverage. Next, we evaluate the order of users search trails while simultaneously incorporating a novel set of metrics that use adjacency of queries issued and Web pages traversed. To evaluate search trails, we incorporated proposed metrics with transactional log data from multiple user studies in which more than 300 users conducted exploratory search tasks on different topics.We demonstrate the effectiveness of the proposed evaluation criteria by measuring how the recommended search trails lead to improvements in both information space coverage and search performance metrics for users across multiple user search datasets. Based on the analysis results, we demonstrate that the order of the recommended search trails plays a significant role and it outperforms the random order of search trails thus being beneficial for the struggling users in improving their overall search effectiveness. We also show that by providing search trail recommendations, users are able to discover more information across multiple facets (in breadth) as well as investigate certain facets in more detail (in depth). These findings provide substantial evidence across multiple datasets to confirm that recommended search trails improve users information seeking coverage and overall knowledge acquisition throughout their search processes.

[1]  Ryen W. White,et al.  Supporting Complex Search Tasks , 2014, CIKM.

[2]  Jae Kyeong Kim,et al.  A literature review and classification of recommender systems research , 2012, Expert Syst. Appl..

[3]  David Bawden,et al.  Information systems and the stimulation of creativity , 1986, J. Inf. Sci..

[4]  Nigel Ford,et al.  Serendipity and information seeking: an empirical study , 2003, J. Documentation.

[5]  Martin Halvey,et al.  Beyond actions: Exploring the discovery of tactics from user logs , 2016, Inf. Process. Manag..

[6]  Qi He,et al.  Web Query Recommendation via Sequential Query Prediction , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[7]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

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

[9]  John M. Budd,et al.  Relevance: Language, Semantics, Philosophy , 2004, Libr. Trends.

[10]  Carol Collier Kuhlthau Inside the Search Process: Information Seeking from the User's Perspective. , 1991 .

[11]  Susan Dunman,et al.  Seeking meaning: A process approach to library and information services , 1996 .

[12]  Rifat Ozcan,et al.  New query suggestion framework and algorithms: A case study for an educational search engine , 2016, Inf. Process. Manag..

[13]  Ryen W. White,et al.  Exploratory Search: Beyond the Query-Response Paradigm , 2009, Exploratory Search: Beyond the Query-Response Paradigm.

[14]  Marcia J. Bates,et al.  The design of browsing and berrypicking techniques for the online search interface , 1989 .

[15]  Doug Downey,et al.  Models of Searching and Browsing: Languages, Studies, and Application , 2007, IJCAI.

[16]  Chirag Shah,et al.  "Implicit search feature based approach to assist users in exploratory search tasks" by Chathra Hendahewa, with Prateek Jainas as coordinator , 2015, Inf. Process. Manag..

[17]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[18]  M. de Rijke,et al.  Learning from homologous queries and semantically related terms for query auto completion , 2016, Inf. Process. Manag..

[19]  Ryen W. White,et al.  Studying the use of popular destinations to enhance web search interaction , 2007, SIGIR.

[20]  Dorota Glowacka,et al.  Is exploratory search different? A comparison of information search behavior for exploratory and lookup tasks , 2016, J. Assoc. Inf. Sci. Technol..

[21]  Ryen W. White,et al.  Assessing the scenic route: measuring the value of search trails in web logs , 2010, SIGIR.

[22]  Nicholas J. Belkin,et al.  Ask for Information Retrieval: Part I. Background and Theory , 1997, J. Documentation.

[23]  Noriko Kando,et al.  Using a concept map to evaluate exploratory search , 2010, IIiX.

[24]  Rodrygo L. T. Santos,et al.  Learning to expand queries using entities , 2014, J. Assoc. Inf. Sci. Technol..

[25]  Philippe A. Palanque,et al.  Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , 2014, International Conference on Human Factors in Computing Systems.

[26]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[27]  T. D. Wilson,et al.  Review of: Kuhlthau, Carol Collier. Seeking meaning: a process approach to library and information services. 2nd. ed. Westport, CT: Libraries Unlimited, 2004 , 2004, Inf. Res..

[28]  M. de Rijke,et al.  Query modeling for entity search based on terms, categories, and examples , 2011, TOIS.

[29]  Masoud Rahgozar,et al.  A query term re-weighting approach using document similarity , 2016, Inf. Process. Manag..

[30]  Jacek Gwizdka,et al.  Exploring the Use of Query Auto Completion: Search Behavior and Query Entry Profiles , 2016, CHIIR.

[31]  George W. Furnas,et al.  Model-driven formative evaluation of exploratory search: A study under a sensemaking framework , 2008, Inf. Process. Manag..

[32]  Ryen W. White,et al.  Studying trailfinding algorithms for enhanced web search , 2010, SIGIR.

[33]  Peter Pirolli,et al.  Information Foraging , 2009, Encyclopedia of Database Systems.

[34]  Christine L. Borgman,et al.  Rethinking Online Monitoring Methods for Information Retrieval Systems: From Search Product to Search Process , 1996, J. Am. Soc. Inf. Sci..

[35]  M. de Rijke,et al.  Behavior‐based personalization in web search , 2017, J. Assoc. Inf. Sci. Technol..

[36]  Pattie Maes,et al.  Footprints: history-rich tools for information foraging , 1999, CHI '99.

[37]  Diane Kelly,et al.  The use of query suggestions during information search , 2014, Inf. Process. Manag..

[38]  Gary Marchionini,et al.  Finding facts vs. browsing knowledge in hypertext systems , 1988, Computer.

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

[40]  Donald G. Saari,et al.  Mathematical structure of voting paradoxes , 2000 .

[41]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[42]  Nicholas J. Belkin,et al.  Exploration of dynamic query suggestions and dynamic search results for their effects on search behaviors , 2012, ASIST.

[43]  Gary Marchionini,et al.  Exploratory search , 2006, Commun. ACM.

[44]  Donald G. Saari,et al.  Mathematical Structure of Voting Paradoxes: II. Positional Voting , 1999 .

[45]  Wai-Tat Fu,et al.  SNIF-ACT: A Model of Information Foraging on the World Wide Web , 2003, User Modeling.