Abstract
The personalized recommendation system is required to save efforts in searching the items in ubiquitous commerce, it is very important for a recommendation system to predict accurately by analyzing user’s preferences. A recommendation system utilizes in general an information filtering technique called collaborative filtering, which is based on the ratings matrix of other users who have similar preference. This paper proposes the user preference through Bayesian categorization for recommendation to overcome the sparsity problem and the first-rater problem of collaborative filtering. In addition, to determine the similarity between the users belonging to a particular class and new users, we assign different statistical values to the items that the users evaluated using Naive Bayesian classifier. We evaluated the proposed method on the EachMovie datasets of user ratings and it was found to significantly outperform the previously proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Balabanovic, M., Shoham, Y.: Fab: Content-based, Collaborative Recommendation. Communication of the Association of Computing Machinery, 66–72 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as Classification: Using Social and Content-based Information in Recommendation. In: Proceedings of the 15th National Conference on AI, pp. 714–720 (1998)
Breese, J.S., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in AI, pp. 43–52 (1998)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems (TOIS) archive 22(1), 5–53 (2004)
Jung, K.Y., Lee, J.H.: User Preference Mining through Hybrid Collaborative Filtering and Content-based Filtering in Recommendation System. IEICE Transaction on Information and Systems 87-D(12), 2781–2790 (2004)
Kim, T.H., Yang, S.B.: An Improved Neighbor Selection Algorithm in Collaborative Filtering. IEICE Trans. on Inf. and Systems E88-D(5), 1072–1076 (2005)
Ko, S.J., Lee, J.H.: Feature Selection using Association Word Mining for Classification. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 211–220. Springer, Heidelberg (2001)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-Boosted Collaborative Filtering. In: Proceedings of the SIGIR-2001 Workshop on Recommender Systems (2001)
McJones, P.: EachMovie collaborative filtering dataset
(1997), URL: http://www.research.digital.com/SRC/eachmovie
Michael, T.: Maching Learning, pp. 154–200. McGraw-Hill, New York (1997)
Miyahara, K., Pazzani, M.J.: Collaborative Filtering with the Simple Bayesian Classifier. In: Proceedings of the 6th Pacific Rim Int. Conference on Artificial Intelligence, pp. 679–689 (2000)
Pazzani, M.J.: A Framework for Collaborative, Content-based and Demographic Filtering. Artificial Intelligence Review, 393–408 (1999)
Soboroff, I., Nicholas, C.K.: Related, but not Relevant: Content-Based Collaborative Filtering in TREC-8. Information Retrieval 5(2–3), 189–208 (2002)
Lee, W.S.: Collaborative Learning for Recommender Systems. In: Proceedings of the 18th International Conference on Machine Learning, pp. 314–321 (1997)
Wang, J., de Vries, A.P., Reinders, M.J.T.: A User-Item Relevance Model for Log-based Collaborative Filtering. In: Proceedings of the European Conference on Information Retrieval, pp. 37–48 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jung, KY. (2006). User Preference Through Bayesian Categorization for Recommendation. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-36668-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36667-6
Online ISBN: 978-3-540-36668-3
eBook Packages: Computer ScienceComputer Science (R0)