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 itemsets 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.