Hybrid Recommendation Algorithm Based on Social Trust Clustering

作     者：Zhu, Jing-Hua Wang, Chao Ma, Sheng-Chao 

作者机构：School of Computer Science and Technology Heilongjiang University Harbin150080 China Heilongjiang Province Key Laboratory for Database and Parallel Computing Harbin150080 China 

出 版 物：《Ruan Jian Xue Bao/Journal of Software》 (Ruan Jian Xue Bao)

年 卷 期：2018年第29卷

页      面：21-31页

核心收录：

基　　金： 基金项目: 国家自然科学基金(6110048) 黑龙江省自然科学基金(F2016034) Foundation item: National Natural Science Foundation of China (6110048) Natural Science Foundation of Heilongjiang Province, China (F2016034) 收稿时间: 2018-05-01 采用时间: 2018-08-30 

主　　题：Collaborative filtering 

摘      要：Recommender system can solve the information overload problem effectively, and collaborative filtering (CF) is one of the techniques that is widely used in recommendation system. However, the traditional CF technology has problems such as poor scalability, sparse data, and low accuracy of recommendation results. In order to improve the quality of recommendations, this article integrates the trust relationship into the recommendation system in which the trust relationship is clustered by using the clustering (FCM) method. Using the trust cluster to predict implicit trust between users, the trust relationship is finally combined with the user-item relationship to give recommendations. The experimental results on the data set of Douban and Epinions show that compared with traditional CF algorithm, trust based recommendation algorithm and recommendation algorithm for user item clustering, the presented algorithm can greatly improve the recommendation quality and time efficiency. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.