Beyond hyperlinks: organizing information footprints in search logs to support effective browsing

While current search engines serve known-item search such as homepage finding very well, they generally cannot support exploratory search effectively. In exploratory search, users do not know their information needs precisely and also often lack the needed knowledge to formulate effective queries, thus querying alone, as supported by the current search engines, is insufficient, and browsing into related information would be very useful. Currently, browsing is mostly done by following hyperlinks embedded on Web pages. In this paper, we propose to leverage search logs to allow a user to browse beyond hyperlinks with a multi-resolution topic map constructed based on search logs. Specifically, we treat search logs as "footprints" left by previous users in the information space and build a multi-resolution topic map to semantically capture and organize them in multiple granularities. Such a topic map can support a user to zoom in, zoom out, and navigate horizontally over the information space, and thus provide flexible and effective browsing capabilities for end users. To test the effectiveness of the proposed methods of supporting browsing, we rely on real search logs and a commercial search engine to implement our proposed methods. Our experimental results show that the proposed topic map is effective to support browsing beyond hyperlinks.

[1]  Wei-Ying Ma,et al.  Learning to cluster web search results , 2004, SIGIR '04.

[2]  Erik Duval,et al.  Towards the integration of a query mechanism and navigation for retrieval of data on multimedia documents , 1992, SIGF.

[3]  ChengXiang Zhai,et al.  Learn from web search logs to organize search results , 2007, SIGIR.

[4]  George W. Furnas,et al.  Effective view navigation , 1997, CHI.

[5]  Susan T. Dumais,et al.  Bringing order to the Web: automatically categorizing search results , 2000, CHI.

[6]  Barry Smyth,et al.  Collecting community wisdom: integrating social search & social navigation , 2007, IUI '07.

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

[8]  Edward A. Fox,et al.  Exploring digital libraries: integrating browsing, searching, and visualization , 2006, Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '06).

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

[10]  Daniela Rus,et al.  Journal of Graph Algorithms and Applications the Star Clustering Algorithm for Static and Dynamic Information Organization , 2022 .

[11]  Xuehua Shen,et al.  Context-sensitive information retrieval using implicit feedback , 2005, SIGIR '05.

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

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

[14]  Doug Downey,et al.  Understanding the relationship between searchers' queries and information goals , 2008, CIKM '08.

[15]  Riccardo Rizzo,et al.  Map-based horizontal navigation in educational Hypertext , 2002, HYPERTEXT '02.

[16]  Xiaojun Jenny Yuan,et al.  Supporting Multiple Information-Seeking Strategies in a Single System Framework , 2006, NAACL.

[17]  Jock D. Mackinlay,et al.  Browsing vs. search: can we find a synergy? (panel session) , 1995, CHI '95.

[18]  Yoelle S. Maarek,et al.  Organizing documents to support browsing in digital libraries , 1995, SIGO.

[19]  Peter Willett,et al.  Recent trends in hierarchic document clustering: A critical review , 1988, Inf. Process. Manag..

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

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

[22]  Monica M. C. Schraefel,et al.  The evolving mSpace platform: leveraging the semantic web on the trail of the memex , 2005, HYPERTEXT '05.

[23]  Christopher Olston,et al.  Navigationaided retrieval , 2007, WWW '07.

[24]  Kevin Li,et al.  Faceted metadata for image search and browsing , 2003, CHI '03.

[25]  Panagiotis G. Ipeirotis,et al.  Automatic Extraction of Useful Facet Hierarchies from Text Databases , 2008, 2008 IEEE 24th International Conference on Data Engineering.

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

[27]  Stephen E. Robertson,et al.  Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval , 1994, SIGIR '94.

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

[29]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[30]  Jimmy J. Lin,et al.  How do users find things with PubMed?: towards automatic utility evaluation with user simulations , 2008, SIGIR '08.

[31]  Christopher Olston,et al.  Navigationaided retrieval , 2007, WWW '07.

[32]  Mark S. Ackerman,et al.  The perfect search engine is not enough: a study of orienteering behavior in directed search , 2004, CHI.

[33]  Susan T. Dumais,et al.  Optimizing search by showing results in context , 2001, CHI.

[34]  ChengXiang Zhai,et al.  Mining term association patterns from search logs for effective query reformulation , 2008, CIKM '08.

[35]  Ji-Rong Wen,et al.  Clustering user queries of a search engine , 2001, WWW '01.

[36]  Thorsten Joachims,et al.  Evaluating Retrieval Performance Using Clickthrough Data , 2003, Text Mining.

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

[38]  Hsinchun Chen,et al.  Internet Browsing and Searching: User Evaluations of Category Map and Concept Space Techniques , 1998, J. Am. Soc. Inf. Sci..

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

[40]  Mehran Sahami,et al.  A web-based kernel function for measuring the similarity of short text snippets , 2006, WWW '06.

[41]  Nicholas J. Belkin,et al.  Interaction with Texts: Information Retrieval as Information-Seeking Behavior , 1993, Information Retrieval.

[42]  Marti A. Hearst,et al.  Reexamining the cluster hypothesis: scatter/gather on retrieval results , 1996, SIGIR '96.

[43]  Jaime G. Carbonell,et al.  Retrieval and feedback models for blog feed search , 2008, SIGIR '08.

[44]  Qiang Yang,et al.  Mining Web Query Hierarchies from Clickthrough Data , 2007, AAAI.

[45]  ChengXiang Zhai,et al.  Massive Implicit Feedback: Organizing Search Logs into Topic Maps for Collaborative Surfing , 2009, UIIR@SIGIR.

[46]  W. Bruce Croft,et al.  Deriving concept hierarchies from text , 1999, SIGIR '99.

[47]  Marti A. Hearst Clustering versus faceted categories for information exploration , 2006, Commun. ACM.

[48]  Ryen W. White,et al.  Mining the search trails of surfing crowds: identifying relevant websites from user activity , 2008, WWW.

[49]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[50]  Oren Etzioni,et al.  Web document clustering: a feasibility demonstration , 1998, SIGIR '98.

[51]  Marti A. Hearst,et al.  Hierarchical faceted metadata in site search interfaces , 2002, CHI Extended Abstracts.

[52]  Susan T. Dumais,et al.  Fast, flexible filtering with phlat , 2006, CHI.

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

[54]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.