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| Matrix Factorization Social Recommendation Algorithm Based on Multiple Social Relationships |
| ZHOU Shuang, BIN Sheng, SUN Gengxin
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| School of data science and software engineering, Qingdao University, Qingdao 266071, China |
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Abstract In real social networks, there are multiple relationships between users. The existing social recommendation algorithms only consider the impact of one relationship on the recommendation results. Based on the multi-subnet composited complex network model, different social relationships among users are introduced into the user feature matrix. In this paper, matrix factorization social recommendation algorithm based on multi-relationship is proposed. By analyzing the experimental results on two real datasets, the social matrix factorization recommendation method with multi-relationship has a significant improvement in recommendation accuracy compared with the traditional matrix factorization algorithm.
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Received: 28 July 2019
Published: 29 April 2020
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