Search Logs as Information Footprints: Supporting Guided Navigation for Exploratory Search

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. In this paper, we present a formal navigation-based retrieval framework to unify querying and browsing and treat both as navigation over topic regions. To support browsing effectively, we treat search logs as “footprints” left by previous users in the information space and build a multi-resolution topic map to guide a user in navigating in the information space. To test the effectiveness of the proposed methods, we build a prototype system based on a small sample of search logs and a commercial search engine. Our experiment results show that the proposed navigationbased framework is promising and the proposed methods for guided navigation are effective.

[1]  Gary Marchionini,et al.  Exploratory search and HCI: designing and evaluating interfaces to support exploratory search interaction , 2007, CHI Extended Abstracts.

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

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

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

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

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

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

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

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

[10]  Ryen W. White,et al.  Supporting Exploratory Search, Introduction, Special Issue, Communications of the ACM , 2006 .

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

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

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

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

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

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

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

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

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

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

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

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

[23]  Gary Marchionini,et al.  Report on ACM SIGIR 2006 workshop on evaluating exploratory search systems , 2006, SIGF.

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

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

[26]  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).

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

[28]  Hajo Hippner,et al.  Text Mining , 2006, Informatik-Spektrum.

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

[30]  Zheng Chen,et al.  Latent semantic analysis for multiple-type interrelated data objects , 2006, SIGIR.

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

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

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

[34]  Ryen W. White,et al.  Query suggestion based on user landing pages , 2007, SIGIR.

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

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

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

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

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

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