SimRate: Improve Collaborative Recommendation Based on Rating Graph for Sparsity

摘要：

Collaborative filtering is a widely used recommending method. But its sparsity problem often happens and makes it defeat when rate data is too few to compute the similarity of users. Sparsity problem also could result into error recommendation. In this paper, the notion of SimRank is used to overcome the problem. Especially, a novel weighted SimRank for rate bi-partite graph, SimRate, is proposed to compute similarity between users and to determine the neighbor users. SimRate still work well for very sparse rate data. The experiments show that SimRate has advantage over state-of-the-art method.

关键词： Collaborative Filtering SimRank Similarity Sparsity

作者: Li Yu Zhaoxin Shu Xiaoping Yang

作者单位: School of Information, Renmin University of China,Beijing 100872, P.R. China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 167-174

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）