A term-based methodology for query reformulation understanding

AbstractKey to any research involving session search is the understanding of how a user’s queries evolve throughout the session. When a user creates a query reformulation, he or she is consciously retaining terms from their original query, removing others and adding new terms. By measuring the similarity between queries we can make inferences on the user’s information need and how successful their new query is likely to be. By identifying the origins of added terms we can infer the user’s motivations and gain an understanding of their interactions. In this paper we present a novel term-based methodology for understanding and interpreting query reformulation actions. We use TREC Session Track data to demonstrate how our technique is able to learn from query logs and we make use of click data to test user interaction behavior when reformulating queries. We identify and evaluate a range of term-based query reformulation strategies and show that our methods provide valuable insight into understanding query reformulation in session search.

[1]  Amanda Spink,et al.  Patterns of query reformulation during Web searching , 2009, J. Assoc. Inf. Sci. Technol..

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

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

[4]  Ben Carterette,et al.  Overview of the TREC 2012 Session Track , 2012, TREC.

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

[6]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[7]  Yiqun Liu,et al.  How do users describe their information need: Query recommendation based on snippet click model , 2011, Expert Syst. Appl..

[8]  GayGeri,et al.  Accurately Interpreting Clickthrough Data as Implicit Feedback , 2017 .

[9]  Jacek Gwizdka,et al.  Analysis and evaluation of query reformulations in different task types , 2010, ASIST.

[10]  Ben Carterette,et al.  Overview of the TREC 2013 Session Track , 2013, TREC.

[11]  Daqing He,et al.  Searching, browsing, and clicking in a search session: changes in user behavior by task and over time , 2014, SIGIR.

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

[13]  Ben Carterette,et al.  Overview of the TREC 2011 Session Track , 2011, TREC.

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

[15]  Ryen W. White,et al.  WWW 2007 / Track: Browsers and User Interfaces Session: Personalization Investigating Behavioral Variability in Web Search , 2022 .

[16]  Nick Craswell,et al.  An experimental comparison of click position-bias models , 2008, WSDM '08.

[17]  Grace Hui Yang,et al.  Utilizing query change for session search , 2013, SIGIR.

[18]  Karen Spärck Jones A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.

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

[20]  Amanda Spink,et al.  Model for organizational knowledge creation and strategic use of information: Research Articles , 2005 .

[21]  Jacek Gwizdka,et al.  Task and user effects on reading patterns in information search , 2011, Interact. Comput..

[22]  Amanda Spink,et al.  A temporal comparison of AltaVista Web searching , 2005, J. Assoc. Inf. Sci. Technol..

[23]  Jun Wang,et al.  Interactive exploratory search for multi page search results , 2013, WWW.

[24]  Hugo Zaragoza,et al.  The Probabilistic Relevance Framework: BM25 and Beyond , 2009, Found. Trends Inf. Retr..

[25]  Jacek Gwizdka,et al.  Linking search tasks with low-level eye movement patterns , 2010, ECCE.

[26]  Dian Tjondronegoro,et al.  Human-computer interaction: the impact of users' cognitive styles on query reformulation behaviour during web searching , 2012, OZCHI.

[27]  Yong Yu,et al.  Identification of ambiguous queries in web search , 2009, Inf. Process. Manag..

[28]  Yiqun Liu,et al.  From Skimming to Reading: A Two-stage Examination Model for Web Search , 2014, CIKM.

[29]  Peter Willett,et al.  Readings in information retrieval , 1997 .