Query Suggestion for Struggling Search by Struggling Flow Graph

We propose a method to generate effective query suggestions aiming to help struggling search, where users experience difficulty in locating information that is relevant to their information need in the search session. The core is identifying struggling component of an on-going struggling session and mining the effective representations of it. The struggling component is the semantic component of information need for which the user struggled to find an effective representation during the struggling session. The proposed method identifies the struggling component of given on-going struggling session and mines the sessions containing the identified struggling component from a query log to build a struggling flow graph. The struggling flow graph records users' reformulation behaviors for the terms of the struggling component, through struggling flow graph we can mine effective representations of the struggling component. The experimental results demonstrate that the proposed method outperforms the baseline methods when it can use two or more queries in a struggling session.

[1]  Yiqun Liu,et al.  Empirical Study on Rare Query Characteristics , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[2]  Ryen W. White,et al.  Struggling or exploring?: disambiguating long search sessions , 2014, WSDM.

[3]  Ryen W. White,et al.  Struggling and Success in Web Search , 2015, CIKM.

[4]  Aristides Gionis,et al.  Improving recommendation for long-tail queries via templates , 2011, WWW.

[5]  Enhong Chen,et al.  Context-aware query suggestion by mining click-through and session data , 2008, KDD.

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

[7]  Fabrizio Silvestri,et al.  Recommendations for the long tail by term-query graph , 2011, WWW.

[8]  Nicholas J. Belkin,et al.  Task difficulty and domain knowledge effects on information search behaviors , 2012, ASIST.

[9]  Elad Yom-Tov,et al.  What makes a query difficult? , 2006, SIGIR.

[10]  Ryen W. White,et al.  Characterizing and predicting search engine switching behavior , 2009, CIKM.

[11]  Benjamin Rey,et al.  Generating query substitutions , 2006, WWW '06.

[12]  Aristides Gionis,et al.  The query-flow graph: model and applications , 2008, CIKM '08.

[13]  Yang Liu,et al.  Adaptive query suggestion for difficult queries , 2012, SIGIR '12.

[14]  Tetsuya Sakai,et al.  Metrics, Statistics, Tests , 2013, PROMISE Winter School.

[15]  Abdur Chowdhury,et al.  A picture of search , 2006, InfoScale '06.

[16]  Doug Beeferman,et al.  Agglomerative clustering of a search engine query log , 2000, KDD '00.

[17]  Kenneth Ward Church,et al.  Query suggestion using hitting time , 2008, CIKM '08.

[18]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

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

[20]  Francesco Bonchi,et al.  Query suggestions using query-flow graphs , 2009, WSCD '09.

[21]  Fabrizio Silvestri,et al.  Efficient query recommendations in the long tail via center-piece subgraphs , 2012, SIGIR '12.

[22]  Rosie Jones,et al.  Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs , 2008, CIKM '08.

[23]  Ricardo A. Baeza-Yates,et al.  Query Recommendation Using Query Logs in Search Engines , 2004, EDBT Workshops.