Helping identify when users find useful documents: examination of query reformulation intervals

We explore search behaviors during a new kind of search unit -- the query reformulation interval (QRI). The QRI is defined as an interval between two consecutive queries in one search session that contains at least two queries. Our controlled, web-based study focused on examining behaviors associated with querying and useful document saving. We compared behavioral variables that characterized QRIs during which useful pages were found with those during which no useful pages were found. Our results demonstrated that the QRI duration and the total time spent on content pages during QRIs with useful pages were significantly longer than during QRIs with no useful pages. Users viewed more content pages and spent more time on content pages than on search result pages during QRIs with useful pages. The findings suggest that user behavior during QRIs can be used as an indicator of QRIs containing useful documents.

[1]  Nicholas J. Belkin,et al.  Display time as implicit feedback: understanding task effects , 2004, SIGIR '04.

[2]  Andrew Trotman,et al.  Focused Access to XML Documents, 6th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2007, Dagstuhl Castle, Germany, December 17-19, 2007. Selected Papers , 2008, INEX.

[3]  JärvelinKalervo,et al.  Task complexity affects information seeking and use , 1995 .

[4]  Jaime Teevan,et al.  Implicit feedback for inferring user preference: a bibliography , 2003, SIGF.

[5]  Susan T. Dumais,et al.  Personalizing Search via Automated Analysis of Interests and Activities , 2005, SIGIR.

[6]  Diane Kelly,et al.  IMPLICIT FEEDBACK: USING BEHAVIOR TO INFER RELEVANCE , 2005 .

[7]  Andrew Trotman,et al.  Focused Access to XML Documents: 6th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2007, Schloss Dagstuhl, Germany , 2008 .

[8]  Filip Radlinski,et al.  Query chains: learning to rank from implicit feedback , 2005, KDD '05.

[9]  Pia Borlund,et al.  The IIR evaluation model: a framework for evaluation of interactive information retrieval systems , 2003, Inf. Res..

[10]  Marcia J. Bates,et al.  Information search tactics , 1979, J. Am. Soc. Inf. Sci..

[11]  Yoichi Shinoda,et al.  Information filtering based on user behavior analysis and best match text retrieval , 1994, SIGIR '94.

[12]  Thorsten Joachims,et al.  Accurately Interpreting Clickthrough Data as Implicit Feedback , 2017 .

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

[14]  Nicholas J. Belkin,et al.  Personalizing information retrieval for multi-session tasks: the roles of task stage and task type , 2010, SIGIR '10.

[15]  Efthimis N. Efthimiadis,et al.  Analyzing and evaluating query reformulation strategies in web search logs , 2009, CIKM.

[16]  Carolyn Watters,et al.  A field study characterizing Web-based information-seeking tasks , 2007 .

[17]  Susan T. Dumais,et al.  Learning user interaction models for predicting web search result preferences , 2006, SIGIR.

[18]  Kalervo Järvelin,et al.  Task complexity affects information seeking and use , 1995 .

[19]  Amanda Spink,et al.  New Directions in Cognitive Information Retrieval , 2005 .

[20]  Diane Kelly Understanding implicit feedback and document preference: a naturalistic user study , 2004, SIGF.

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

[22]  Ryen W. White,et al.  A study on the effects of personalization and task information on implicit feedback performance , 2006, CIKM '06.

[23]  Elaine Toms,et al.  Task Effects on Interactive Search: The Query Factor , 2008, INEX.

[24]  Anne Aula,et al.  How does search behavior change as search becomes more difficult? , 2010, CHI.