Studying the use of popular destinations to enhance web search interaction

We present a novel Web search interaction feature which, for a given query, provides links to websites frequently visited by other users with similar information needs. These popular destinations complement traditional search results, allowing direct navigation to authoritative resources for the query topic. Destinations are identified using the history of search and browsing behavior of many users over an extended time period, whose collective behavior provides a basis for computing source authority. We describe a user study which compared the suggestion of destinations with the previously proposed suggestion of related queries, as well as with traditional, unaided Web search. Results show that search enhanced by destination suggestions outperforms other systems for exploratory tasks, with best performance obtained from mining past user behavior at query-level granularity.

[1]  Amanda Spink,et al.  U.S. versus European web searching trends , 2002, SIGF.

[2]  Pedro M. Domingos,et al.  Adaptive Web Navigation for Wireless Devices , 2001, IJCAI.

[3]  Nicholas J. Belkin,et al.  A case for interaction: a study of interactive information retrieval behavior and effectiveness , 1996, CHI.

[4]  José Luis Vicedo González,et al.  TREC: Experiment and evaluation in information retrieval , 2007, J. Assoc. Inf. Sci. Technol..

[5]  George W. Furnas,et al.  Experience with an adaptive indexing scheme , 1985, CHI '85.

[6]  Gary Marchionini,et al.  Examining the effectiveness of real-time query expansion , 2007, Inf. Process. Manag..

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

[8]  Monika Henzinger,et al.  Analysis of a very large web search engine query log , 1999, SIGF.

[9]  Amanda Spink,et al.  Multitasking during Web search sessions , 2006, Inf. Process. Manag..

[10]  Nicholas J. Belkin,et al.  The TREC Interactive Tracks: Putting the User into Search , 2005 .

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

[12]  Sanda M. Harabagiu,et al.  FERRET: Interactive Question-Answering for Real-World Environments , 2006, ACL.

[13]  Peter G. Anick Using terminological feedback for web search refinement: a log-based study , 2003, SIGIR.

[14]  Pia Borlund,et al.  Experimental components for the evaluation of interactive information retrieval systems , 2000, J. Documentation.

[15]  Susan T. Dumais,et al.  Improving Web Search Ranking by Incorporating User Behavior Information , 2019, SIGIR Forum.

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

[17]  Barry Smyth,et al.  Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine , 2004, User Modeling and User-Adapted Interaction.

[18]  Robin Jeffries,et al.  Orienteering in an information landscape: how information seekers get from here to there , 1993, INTERCHI.

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

[20]  Micheline Beaulieu,et al.  Experiments on interfaces to support query expansion , 1997, J. Documentation.

[21]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

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