A Personalized Recommendation Algorithm Based on Associative Sets
ABSTRACT
During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users’ real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to solve this problem. It uses frequent item sets to filter out noise data, and makes recommendations according to users’ real preferences, so as to enhance the accuracy of recommending results. Test results have proved the superiority of this algorithm.
During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users’ real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to solve this problem. It uses frequent item sets to filter out noise data, and makes recommendations according to users’ real preferences, so as to enhance the accuracy of recommending results. Test results have proved the superiority of this algorithm.
Cite this paper
nullG. JIANG, H. QING and T. HUANG, "A Personalized Recommendation Algorithm Based on Associative Sets," Journal of Service Science and Management, Vol. 2 No. 4, 2009, pp. 400-403. doi: 10.4236/jssm.2009.24048.
nullG. JIANG, H. QING and T. HUANG, "A Personalized Recommendation Algorithm Based on Associative Sets," Journal of Service Science and Management, Vol. 2 No. 4, 2009, pp. 400-403. doi: 10.4236/jssm.2009.24048.
References
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[1] J. B. Schafer, J. A. Konstan, and J. Riedl, “E-Commerce recommendation applications,” Journal of Data Mining and Knowledge Discovery, pp. 115–153, 2001. [2] J. Breese, D. Hecherman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 43–52, 1998. [3] L. Zhao, N. J. Hu, and S. Z. Zhang, “Algorithm design for personalization recommendation systems,” Journal of Com- puter Research and Development, pp. 986–991, August 2002. [4] Y. Li, L. Liu, and X. F. Li, “Research on personalized recommendation algorithm for user’s multiple interests,” Journal of Computer Integrated Manufacturing Systems, pp. 1610–1615, December 2004. [5] J. W. Han and M. Kamber, “Data mining concepts and techniques (Second Edition),” Ming Fang, Xiaofeng Meng translated, China Machine Press, pp. 149–161, March 2007. [6] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item based collaborative filtering recommendation algo-rithms,” In Proceedings of the Tenth International World Wide Web Conference, pp. 285–295, 2001.