A Knowledge Based Recommender System Based on Consistent Preference Relations
E-commerce companies have developed many methods and tools in order to personalize their web sites and services according to users’ necessities and tastes. The most successful and widespread are the recommender systems. The aim of these systems is to lead people to interesting items through recommendations. Sometimes, these systems face situations in which there is a lack of information and this implies unsuccessful results. In this chapter we propose a knowledge based recommender system designed to overcome these situations. The proposed system is able to compute recommendations from scarce information. Our proposal will consist in gathering user’s preference information over several examples using an incomplete preference relation. The system will complete this relation and exploit it in order to obtain a user profile that will be utilized to generate good recommendations.
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- 2.B. De Baets, B. Van de Walle, and E. Kerre. Fuzzy preference structures without incomparability. Fuzzy Sets and Systems, (76):333–348, 1995.Google Scholar
- 3.C. Basu, H. Hirsh, and W. Cohen. Recommendation as classification: Using social and contentbased information in recommendation. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 714–720, 1998.Google Scholar
- 4.R. Burke. Knowledge-based recommender systems. Encyclopedia of Library and Information Systems, 69(32), 2000.Google Scholar
- 6.F. Chiclana, F. Herrera, and E. Herrera-Viedma. Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations. Fuzzy Sets and Systems, (97):33–48, 1998.Google Scholar
- 8.H.R. Guttman. Merchant Differentiation through Integrative Negotiation in Agent-mediated Electronic Commerce. Master’s thesis, School of Architecture and Planning, Program in Media Arts and Sciences, Massachusetts Institute of Technology, 1998.Google Scholar
- 9.E. Herrera-Viedma, F. Herrera, F. Chiclana, and M. Luque. Some issues on consistency of fuzzy preference relations. European Journal of Operational Research, (154):98–109, 2004.Google Scholar
- 11.J. Kolodner. Case-Based Reasoning. Morgan Kaufmann, 1993.Google Scholar
- 12.B. Krulwich. Lifestyle finder: intelligent user profiling using large-scale demographic data. AI Magazine, 18(2):37–45, 1997.Google Scholar
- 15.R.J. Mooney and L. Roy. Content-based book recommending using learning text categorization. In Proceedings of the Fifth ACM Conference on Digital Libraries, pages 195–204, 2000.Google Scholar
- 17.B.M. Sarwar, G. Karypis, J.A. Konstan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In ACM Conference on Electronic Commerce, pages 158–167, 2000.Google Scholar
- 18.T. Tanino. Non-Conventional Preference Relations in Decision Making, chapter Fuzzy Preference Relations in Group Decision Making, pages 54–71. Springer-Verlag, New York, 1988.Google Scholar
- 19.Y. Wang, Y. Chuang, M. Hsu, and H. Keh. A personalized recommender system for cosmetic business. Expert Systems with Applications, (26):427–434, 2004.Google Scholar
- 22.Y. Yang. Expert network: Effective and efficient learning from human decisions in text categorization and retrieval. In Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland, 3-6 July 1994 (Special Issue of the SIGIR Forum), pages 13–22. ACM/Springer, 1994.Google Scholar