Collaborative Web Search (CWS) seeks to exploit the high degree of natural query repetition and result selection regularity that is prevalent among communities of searchers. CWS reuses the search experiences of community members, to promote results that have previously been judged relevant for queries. This facilitates a better response to the type of vague queries that are commonplace in Web search and allows a generic search engine to adapt to the preferences of communities of individuals. CWS contemplates a society of search communities, each with its own repository of experience. In this paper we describe and evaluate a new technique for leveraging the search experiences of related communities as sources of additional search knowledge.


Query Term Host Community Related Community Search Session Successful Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Lawrence, S., Giles, C.L.: Context and Page Analysis for Improved Web Search. IEEE Internet Computing, 38–46 (July-August 1998)Google Scholar
  2. 2.
    Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T.: The vocabulary problem in human-system communication. Communications of the ACM 30, 964–971 (1987)CrossRefGoogle Scholar
  3. 3.
    Smyth, B., Balfe, E., Boydell, O., Bradley, K., Briggs, P., Coyle, M., Freyne, J.: A Live-User Evaluation of Collaborative Web Search. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), Edinburgh, Scotland, pp. 1419–1424 (2005)Google Scholar
  4. 4.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  5. 5.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7, 39–59 (1994)Google Scholar
  6. 6.
    Smyth, B., Balfe, E., Freyne, J., Briggs, P., Coyle, M., Boydell, O.: Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine. User Modeling and User-Adaptated Interaction 14, 383–423 (2004)CrossRefGoogle Scholar
  7. 7.
    Leake, D.B., Sooriamurthi, R.: When Two Case Bases are Better Than One: Exploiting Multiple Case-Bases. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 321–335. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    McGinty, L., Smyth, B.: Collaborative Case-Based Reasoning: Applications in Personalized Route Planning. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 362–376. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Prasad, M.N., Plaza, E.: Corporate Memories as Distributed Case Libraries. In: Proceedings of the Corporate Memory and Enterprise Modeling Track in the 10th Knowledge Acquisition Workshop (1996)Google Scholar
  10. 10.
    Gravano, L., Chang, C.C.K., García-Molina, H., Paepcke, A.: STARTS: Stanford proposal for Internet meta-searching. In: Proceedings of the 1997 ACM SIGMOD Conference, 1997, pp. 207–218 (1997)Google Scholar
  11. 11.
    Joseph, S.: NeuroGrid: Semantically Routing Queries in Peer-to-Peer Networks. LNCS, vol. 2376, pp. 202–214. Springer, Heidelberg (2002)Google Scholar
  12. 12.
    Dreilinger, D., Howe, A.E.: Experiences with Selecting Search Engines Using Meta Search. ACM Transactions on Information Systems 15(3), 195–222 (1997)CrossRefGoogle Scholar
  13. 13.
    Zamir, O., Etzioni, O.: Grouper: Dynamic Clustering Interface to Web Search Results. Computer Networks 31(11-16), 1361–1374 (1999)CrossRefGoogle Scholar
  14. 14.
    Zhang, D., Dong, Y.: Semantic, Hierarchical, Online Clustering of Web Search Results. In: Proceedings of the 6th Asia Pacific Web Conference (APWEB), Hangzhou, China, pp. 69–78 (2004)Google Scholar
  15. 15.
    Hamilton, N.: The mechanics of a Deep Net Metasearch Engine. In: Proceedings of the 12th International World Wide Web Conference, Budapest, Hungary (2003)Google Scholar
  16. 16.
    Freyne, J., Smyth, B.: Communities, Collaboration and Cooperation in Personalized Web Search. In: Proceedings of the 3rd Workshop on Intelligent Techniques for Web Personalization (ITWP 2005) in conjunction with the 19th International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, pp. 73–80 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jill Freyne
    • 1
  • Barry Smyth
    • 1
  1. 1.School of Computer Science and InformaticsUniversity College DublinDublin 4Ireland

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