Research of Collaborative Filtering Recommendation Algorithm Based on Trust Propagation Model

摘要：

Traditional collaborative filtering recommendation algorithm is one of the methods to solve the information overloading problem in E-Commerce. However, there are four urgent problems in this algorithm namely data sparse, cold start, attack-resistant and scalability. This paper makes a trust propagation model called TPM; proposes a hybrid index called TS index and a novel collaborative filtering recommendation algorithm called TPCF using TPM and TS index. The results of experiments using the dataset of Epinions.com, a popular ecommerce review website, show that TPCF is more attackresistant and improves the precision and coverage rate compared with the traditional collaborative filtering recommendation algorithm using Pearsons correlation coefficient. TPCF has a better performance against the traditional collaborative filtering recommendation algorithm on the problems of data sparse, cold start and attack-resistant.

关键词： recommender systems collaborative filtering trust network trust propagation model data sparse

作者: Xiao Cheng Chen Run Jia Liu Hui You Chang

作者单位: School of Information Science and Technology, Sun Yat-sen University Guangzhou, China Software School, Sun Yat-sen University Guangzhou, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 177-183

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）