• 学术研究 •

### 融合社交网络特征的协同过滤推荐算法

1. 1. 天津大学 电子信息工程学院，天津 300072
2. 天津大学 信息与网络中心，天津 300072
• 出版日期:2018-02-01 发布日期:2018-01-31

### Collaborative Filtering Recommendation Algorithm Based on Characteristics of Social Network

GUO Ningning1, WANG Baoliang1+, HOU Yonghong1, CHANG Peng2

1. 1. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China
2. Information and Network Center, Tianjin University, Tianjin 300072, China
• Online:2018-02-01 Published:2018-01-31

Abstract: To solve the severe sparseness problem of traditional collaborative filtering recommendation algorithm, this paper proposes a novel collaborative filtering recommendation algorithm based on the characteristics of social network. On the basis of traditional matrix decomposition model, the algorithm obtains the trust and trusted characteristic matrix by integrating the characteristics of social network and user??s preference degree, and then, predicts the rating of the commodity by the social identity matrix, the commodity characteristic matrix and the user rating preference similarity in common. In order to verify the reliability of the proposed algorithm, this paper uses the Epinions open dataset to compare the algorithm performance. The experimental results show that compared with the existing social recommendation algorithms, the proposed algorithm has smaller average absolute error and root mean square error. Meanwhile, there is a linear relationship between the time complexity of the proposed algorithm and the number of the dataset. Therefore, the proposed algorithm can effectively reduce the impact of data sparseness on recommendation results and improve the recommendation accuracy rate. In practice, the proposed algorithm can be considered as an alternative and development of the large-scale data set recommendation.