A Personalized Recommendation Algorithm with User Trust in Social Network  

作　　者：Yuxin Dong Chunhui Zhao Weijie Cheng Liang Li Lin Liu 

机构地区：[1]College of Computer Science and Technology,Harbin Engineering University,Harbin150001,Heilongjiang,China [2]College of Information and Communication Engineering,Harbin Engineering University,Harbin150001,Heilongjiang,China

出　　处：《国际计算机前沿大会会议论文集》2016年第1期20-22,共3页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

基　　金：This work is supported by the National Natural Science Foundation of China under Grants No. 61272186 and the Foundation of Heilongjiang Postdoctoral under Grant No. LBH-Z12068.

摘　　要：In the era of big data, personalized recommendation has become an important research issue in social networks as it can find and match user’s preference. In this paper, the user trust is integrated into the recommendation algorithm, by dividing the user trust into 2 parts: user score trust and user preference trust. In view of the common items in user item score matrix, the algorithm combines the number of items with the score similarity between users, and establishes an asymmetric trust relationship matrix so as to calculate the user’s score trust. For the non common score items, we use the attribute information of items and the scoring weight to calculate the user’s preference trust. Based on the user trust in social network, a new collaborative filtering recommendation algorithm is proposed. Besides, a new matrix factorization recommendation algorithm is proposed by combining the user trust with matrix factorization. We did the experiments comparing with the related algorithms on the real data sets of social network. The results show that the proposed algorithms can effectively improve the accuracy of recommendation.

关 键 词：RECOMMENDATION system COLLABORATIVE FILTERING Matrix FACTORIZATION User TRUST SOCIAL network 

分 类 号：C5[社会学]