Stuart E. Middleton
Nigel R. Shadbolt
ABSTRACT
Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a diverse and dynamic environment. Recommender systems help where explicit search queries are not available or are difficult to formulate, learning the type of thing users like over a period of time.We explore an ontological approach to user profiling in the context of a recommender system. Building on previous work involving ontological profile inference and the use of external ontologies to overcome the cold-start problem, we explore the idea of profile visualization to capture further knowledge about user interests. Our system, called Foxtrot, examines the problem of recommending on-line research papers to academic researchers. Both our ontological approach to user profiling and our visualization of user profiles are novel ideas to recommender systems. A year long experiment is conducted with over 200 staff and students at the University of Southampton. The effectiveness of visualizing profiles and eliciting profile feedback is measured, as is the overall effectiveness of the recommender system.
- Aha, D. Kibler, D. Albert, M. "Instance-based learning algorithms". Machine Learning, 6:37--66, (1991)]] Google Scholar
Digital Library
- Balabanović, M. Shoham, Y. "Fab: Content-Based, Collaborative Recommendation". Communications of the ACM 40(3), 67--72, (1997)]] Google ScholarDigital Library
- Billsus, D. Pazzani, M. J. "A Personal News Agent that Talks, Learns and Explains". In Autonomous Agents 98, Minneapolis MN USA, (1998)]] Google ScholarDigital Library
- Bollacker, K. D. Lawrence, S. Giles, C. L. "CiteSeer: An Autonomous Web Agent for Automatic Retrieval and Identification of Interesting Publications". In Autonomous Agents 98, Minneapolis MN USA, (1998)]] Google ScholarDigital Library
- Freund, Y. Schapire, R. E. "Experiments with a New Boosting Algorithm". Proceedings of the Thirteenth International Conference on Machine Learning, (1996)]]Google Scholar
- Konstan, J. A. Miller, B. N. Maltz, D. Herlocker, J. L. Gordon, L. R. Riedl, J. "GroupLens: Applying Collaborative Filtering to Usenet News". Communications of the ACM 40(3), 77--87, (1997)]] Google ScholarDigital Library
- Lang, K. "NewsWeeder: Learning to Filter NetNews". In ICML95 Conference Proceedings, 331--339, (1995)]]Google Scholar
- McCallum, A. K. Nigam, K. Rennie, J. Seymore, K. "Automating the Construction of Internet Portals with Machine Learning". Information Retrieval 3(2), pages 127--163, (2000)]] Google ScholarDigital Library
- Middleton, S. E. Alani, H. Shadbolt, N. R. De Roure, D.C. "Exploiting Synergy Between Ontologies and Recommender Systems". International Workshop on the Semantic Web, Proceedings of the 11th International World Wide Web Conference WWW-2002, Hawaii, USA, (2002)]]Google Scholar
- Middleton, S. E. De Roure, D. C. Shadbolt, N. R. "Capturing Knowledge of User Preferences: ontologies on recommender systems". In Proceedings of the First International Conference on Knowledge Capture (K-CAP 2001), Victoria, B.C. Canada, (2001)]] Google ScholarDigital Library
- Mladenić, D. "Personal WebWatcher: design and implementation". Technical Report IJS-DP-7472, Department for Intelligent Systems, J. Stefan Institute, (1996)]]Google Scholar
- Porter, M. "An algorithm for suffix stripping". Program 14 (3), pp. 130--137, (1980)]]Google ScholarCross Ref
- Sebastiani, F. "Machine learning in automated text categorization". ACM Computing Surveys, (2002)]] Google ScholarDigital Library
- SMART Staff "User's Manual for the SMART Information Retrieval System". Technical Report 71--95, Revised April 1974, Cornell University (1974)]]Google Scholar
Index Terms
- Capturing interest through inference and visualization: ontological user profiling in recommender systems
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