A Personalized Recommendation System Combining Case-Based Reasoning and User-Based Collaborative Filtering

摘要：

Personalized recommendation systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering is one of the most important technologies in recommender systems. Such technology recommends items to a customer according to the preference data of similar customers. The main problems of collaborative filtering are about prediction accuracy and data sparsity. To solve these problems, this paper presents a personalized recommendation algorithm combining case-based reasoning and user-based collaborative filtering. Firstly, it employs case-based reasoning technology to fill the vacant ratings of the user-item matrix. Then, it produces prediction of the target user to the target item using user-based collaborative filtering. The personalized recommendation system combining case-based reasoning and user-based collaborative filtering can alleviate the sparsity issue and can produce more accuracy recommendation than the traditional recommender systems.

关键词： Personalized Recommendation System Collaborative Filtering Sparsity Case-based Reasoning

作者: XiaoMing Zhu HongWu Ye SongJie Gong

作者单位: Zhejiang Business Technology Institute, Ningbo 315012, P. R. China Zhejiang Textile & Fashion College, Ningbo 315211, P. R. China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 4026-4028

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）