Building Complete Collaborative Filtering Method System

摘要：

Collaborative filtering (CF) is a key technique in recommender system. Recently, general neighborhood problem existing in collaborative filtering is identified in our previous work, which could result into fatal wrong under multicommunity or multi-interest case. In order to overcome it, collaborative filtering based on community (CFC) is presented. Unfortunately, CFC suffers from severer sparsity, which could result into worse performance. Various improved methods are proposed to enhance it. Based on a series of above methods, a complete and hierarchical Collaborative Filtering Method System (CFMS) is build. CFMS extend collaborative filtering, adapting to various different cases. Experiments are made to empirically valuate and compare various methods of CFMS.

作者: Li Yu Xiaoping Yang

作者单位: Information School Renmin University of China Beijing, PR.China

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 412-417

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）