AN IMPROVED COLLABORATIVE FILTERING RECOMMENDATION ALGORITHM BASED ON FACTOR OF CREDIT

摘要：

Traditional collaborative filtering algorithm is a weighted average prediction algorithm based on nearest neighbors ratings. Besides similarity between users, trust and credit are also parameters to affect recommendation. This paper proposes a computational model of credit factor and then a collaborative filtering algorithm based on it. This model is based on trust factor and takes credit model as the basic elements. This proposed algorithm further improves the validity and accuracy of the recommendation.

关键词： Collaborative Filtering Credit Trust Similarity Nearest Neighbor

作者: Haiwei Tong Tingjie Lv Pei Huang

作者单位: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100 School of Humanities, Beijing University of Posts and Telecommunications, Beijing, 100876

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: 424-429

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）