A Personalized Recommendation Algorithm with User Trust in Social Network

• 作者: Chunhui Zhao Liang Li Lin Liu Weijie Cheng Yuxin Dong
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: RECOMMENDATION system COLLABORATIVE FILTERING Matrix FACTORIZATION USER TRUST Social network
• 年，卷（期）: 2016,（1）
• 页码范围: 20-22
• 页数: 3页
• 分类号: C5

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.