Further Experiments on Collaborative Ranking in Community-Based Web Search

As the search engine arms-race continues, search engines are constantly looking for ways to improve the manner in which they respond to user queries. Given the vagueness of Web search queries, recent research has focused on ways to introduce context into the search process as a means of clarifying vague, under-specified or ambiguous query terms. In this paper we describe a novel approach to using context in Web search that seeks to personalize the results of a generic search engine for the needs of a specialist community of users. In particular we describe two separate evaluations in detail that demonstrate how the collaborative search method has the potential to deliver significant search performance benefits to end-users while avoiding many of the privacy and security concerns that are commonly associated with related personalization research.

[1]  C. Lee Giles,et al.  Accessibility of information on the Web , 2000, INTL.

[2]  Krishna Bharat.  SearchPad: explicit capture of search context to support Web search , 2000, Comput. Networks.

[3]  Nicholas Kushmerick,et al.  Wrapper Induction for Information Extraction , 1997, IJCAI.

[4]  Steve Lawrence,et al.  Context in Web Search , 2000, IEEE Data Eng. Bull..

[5]  Barry Smyth,et al.  Distributing Case‐Base Maintenance: The Collaborative Maintenance Approach , 2001, Comput. Intell..

[6]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[7]  Taher H. Haveliwala.  Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[8]  Chris Buckley,et al.  Improving automatic query expansion , 1998, SIGIR '98.

[9]  William P. Birmingham,et al.  Improving category specific Web search by learning query modifications , 2001, Proceedings 2001 Symposium on Applications and the Internet.

[10]  Barry Smyth,et al.  Collaborative Web Search , 2009, IJCAI.

[11]  C. Lee Giles,et al.  Searching the Web: general and scientific information access , 1999, First IEEE/POPOV Workshop on Internet Technologies and Services. Proceedings (Cat. No.99EX391).

[12]  C. Lee Giles,et al.  Context and Page Analysis for Improved Web Search , 1998, IEEE Internet Comput..

[13]  Henry Lieberman,et al.  Letizia: An Agent That Assists Web Browsing , 1995, IJCAI.

[14]  Ehud Rivlin,et al.  Placing search in context: the concept revisited , 2002, TOIS.

[15]  Barry Smyth,et al.  Case-Based User Profiling for Content Personalisation , 2000, AH.

[16]  Neil J. Hurley,et al.  An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering , 2004, Artificial Intelligence Review.

[17]  Adele E. Howe,et al.  Experiences with selecting search engines using metasearch , 1997, TOIS.

[18]  C. Lee Giles,et al.  DEADLINER: building a new niche search engine , 2000, CIKM '00.

[19]  Thad Starner,et al.  Remembrance Agent: A Continuously Running Automated Information Retrieval System , 1996, PAAM.

[20]  Michael D. Gordon,et al.  Web Search---Your Way , 2001, CACM.

[21]  Kristian J. Hammond,et al.  User interactions with everyday applications as context for just-in-time information access , 2000, IUI '00.

[22]  Oren Etzioni,et al.  The MetaCrawler architecture for resource aggregation on the Web , 1997 .