AN OPTIMIZED ITEM-BASED COLLABORATIVE FILTERING RECOMMENDATION ALGORITHM

摘要：

Collaborative filtering is a very important technology in E-commerce. Unfortunately, with the increase of users and commodities, the user rating data is extremely sparse, which leads to the low efficient collaborative filtering recommendation system. To address these issues, an optimized collaborative filtering recommendation algorithm based on item is proposed. While calculating the similarity of two items, we obtain the ratio of users who rated both items to those who rated each of them. The ratio is taken into account in this method. The experimental results show that the proposed algorithm can improve the quality of collaborative filtering.

关键词： Personalized Recommendation Itembased collaborative filtering item similarity MAE

作者: Jinbo Zhang Zhiqing Lin Bo Xiao Chuang Zhang

作者单位: Pattern Recognition & Intelligent System Lab,Beijing University of Posts and Telecommunications, Beijing

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

会议地点: 北京

会议语种:英文

页码: 414-418

在线出版日期: 2009-11-06（万方平台首次上网日期，不代表论文的发表时间）