Further Experiments in Case-Based Collaborative Web Search
- 588 Downloads
Collaborative Web Search (CWS) proposes a case-based approach to personalizing search results for the needs of a community of like-minded searchers. The search activities of users are captured as a case base of search cases, each corresponding to community search behaviour (the results selected) for a given query. When responding to a new query, CWS selects a set of similar cases and promotes their selected results within the final result-list. In this paper we describe how this case-based view can be broadened to accommodate suggestions from multiple case bases, reflecting the expertise and preferences of complementary search communities. In this way it is possible to supplement the recommendations of the host community with complementary recommendations from related communities. We describe the results of a new live-user trial that speaks to the performance benefits that are available by using multiple case bases in this way compared to the use of a single case base.
KeywordsHost Community Related Community Recommendation List Similar Query Test Query
Unable to display preview. Download preview PDF.
- 1.Ashley, K.D.: Modeling Legal Argument: Reasoning with Cases and Hypotheticals. Technical 88–01, Department of Computer and Information Science, University of Massachusetts, Amherst, MA (1990)Google Scholar
- 3.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 (IJCAI 2005), Edinburgh, Scotland, pp. 73–80 (2005)Google Scholar
- 6.Hamilton, N.: The mechanics of a Deep Net Metasearch Engine. In: Proceedings of the 12th International World Wide Web Conference (Posters), Budapest, Hungary (April 2003)Google Scholar
- 7.Kanawati, R., Jaczynski, M., Trousse, B., Andreoli, J.-M.: Applying the Broadway Recommendation Computation Approach for Implementing a Query Refinement Service in the CBKB Meta-search Engine. In: Coinference Fraicaise sur le Raisonnement a Partir de Cas, RaPC 1999 (1999)Google Scholar
- 8.Lawrence, S., Giles, C.L.: Context and Page Analysis for Improved Web Search.. IEEE Internet Computing, 38–46 (July-August, 1998)Google Scholar
- 10.Leuski, A.: Evaluating document clustering for interactive information retrieval.. In: Paques, L.L.H., Grossman, D. (eds.) Proceedings of 10th International Conference on Information and Knowledge Management (CIKM 2001), Atlanta, Georgia, USA, pp. 41–48. ACM Press, New York (2001)Google Scholar
- 12.Nagendra Prasad, M.V., Lesser, V.R., Lander, S.E.: Retrieval and Reasoning in Distributed Case Bases. Journal of Visual Communication and Image Representation, Special Issue on Digital Libraries 7(1), 74–87 (1996)Google Scholar
- 13.Nagendra Prasad, M.V., 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
- 15.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