Abstract
Recommendation is a popular and hot problem in e-commerce. Recommendation systems are realized in many ways such as content-based recommendation, collaborative filtering recommendation, and hybrid approach recommendation. In this article, a new collaborative filtering recommendation algorithm based on naive Bayesian method is proposed. Unlike original naive Bayesian method, the new algorithm can be applied to instances where conditional independence assumption is not obeyed strictly. According to our experiment, the new recommendation algorithm has a better performance than many existing algorithms including the popular k-NN algorithm used by Amazon.com especially at long length recommendation.
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References
Adomavicius, G., Tuzhilin, A.: The next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering (2005)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing (2003)
Makhoul, J., Kubala, F., Schwartz, R., Weischedel, R.: Performance measures for information extraction. In: Proceedings of Broadcast News Workshop 1999 (1999)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. 14th Conf. Uncertainty in Artificial Intelligence (July 1998)
Hofmann, T.: Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis. In: Proc. 26th Ann. Int’l ACM SIGIR Conf. (2003)
Kotsiantis, S.B., Zaharakis, I.D., Pintelas, P.E.: Machine learning: a review of classification and combining techniques. Artificial Intelligence Review (2006)
Yuxia, H., Ling, B.: A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet. Expert System With Applications (2009)
Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM (March 1997)
Koren, Y.: Factorization Meets the Neighborhood: a MultifacetedCollaborative Filtering Model. ACM, New York (2008)
Schafer, J.B., Konstan, J.A., Reidl, J.: E-Commerce Recommendation Applications. In: Data Mining and Knowledge Discovery. Kluwer Academic, Dordrecht (2001)
Pernkopf, F.: Bayesian network classifiers versus selective k-NN classifier. Pattern Recognition (January 2005)
Balabanovic, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Comm. ACM (1997)
Rocchio, J.J.: Relevance Feedback in Information Retrieval. In: Salton, G. (ed.) SMART Retrieval System-Experiments in Automatic Document Processing, ch. 14. Prentice Hall, Englewood Cliffs (1979)
Pazzani, M., Billsus, D.: Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27, 313–331 (1997)
Littlestone, N., Warmuth, M.: The Weighted Majority Algorithm. Information and Computation 108(2), 212–261 (1994)
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Wang, K., Tan, Y. (2011). A New Collaborative Filtering Recommendation Approach Based on Naive Bayesian Method. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_26
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DOI: https://doi.org/10.1007/978-3-642-21524-7_26
Publisher Name: Springer, Berlin, Heidelberg
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