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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.

has been cited by the following article:

• TITLE: A Personalized Recommendation Algorithm Based on Associative Sets

  AUTHORS: Guorui JIANG, Hai QING, Tiyun HUANG

  KEYWORDS: Frequent Itemsets, Associative Sets, Collaborative Filtering, Recommendation Technology

  JOURNAL NAME: Journal of Service Science and Management, Vol.2 No.4, December 15, 2009

  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.