A Personalized Recommendation Algorithm Based on Associative Sets

摘要：

During the process of personalized recommendation,some items evaluated by users are performed by accident,in other words,they have little correlation with usersreal 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.

关键词： frequent itemsets associative sets collaborative filtering recommendation technology

作者: Guorui Jiang Hai Qing Tiyun Huang

作者单位: School of Economics and Management,Beijing University of Technology,Beijing,100124,P.R.China

会议类型: 国际会议

会议名称: 第11届信息学与组织符号学国际会议(11th International Conference on Informations and Semiotics in Organisations)

会议地点: 北京

会议语种:英文

页码: 190-195

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）