Exploiting Similarity Measures in Multi-criteria Based Recommendations
The need for developing efficient and effective recommender systems has lately become fundamental, basically due to the vast amount of on-line information and the increasing popularity of Internet applications. Such systems are based on various recommendation techniques, which aim at guiding users to survey objects that appear as interesting or useful to them. By exploiting the concept of fuzzy similarity measures, this paper presents a recommendation framework that builds on the strengths of knowledge-based and collaborative filtering techniques. Following a multi-criteria approach, the proposed framework is able to provide users with a ranked list of alternatives, while it also permits them to submit their evaluations on the existing objects of the database. Much attention is given to the extent in which the user evaluation may affect the values of the stored objects. The applicability of our approach is demonstrated through a web-based tool that provides recommendations about visiting different cities of a country.
Unable to display preview. Download preview PDF.
- 2.Burke, R.: Ranking algorithms for costly similarity measures. In: Aha, D., Watson, I., Yang, Q. (eds.) Case-Based Reasoning Research and Development (Proc. of the 4th Intern. Conference on Case-Based Reasoning), pp. 105–117. Springer, New York (2001)Google Scholar
- 3.Burke, R.: The Wasabi Personal Shopper: A Case-Based Recommender System. In: Proceedings of the 11th Innovative Applications of Artificial Intelligence Conference on Artificial Intelligence (IAAI 1999), Orlando, Florida, pp. 844–849. AAAI Press, Menlo Park (1999)Google Scholar
- 5.Cotter, P., Smyth, B.: PTV Intelligent Personalised TV Guides. In: Proceedings of the 12th Conference on Innovative Applications of Artificial Intelligence (IAAI 2000), Austin Texas, pp. 957–964. AAAI Press, Menlo Park (2000)Google Scholar
- 8.Kurapati, K., Gutta, S., Schaffer, D., Martino, J., Zimmerman, J.: A multi- Agent TV Recommender. In: Proceedings of the UM 2001 Workshop on Personalization in Future TV. Sonthofen, Germany (2001)Google Scholar
- 9.Nahm, U., Mooney, R.: Text Mining with Information Extraction. In: Proc. of Spring Symposium on Mining Answers from Texts and Knowledge Bases, Stanford, CA, pp. 60–68 (2002)Google Scholar
- 12.Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the 14th Intern. Conference on Machine Learning, pp. 412–420 (1997)Google Scholar