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DOI码：10.1016/j.future.2018.04.079

所属单位：(1) Computer School, Wuhan University, Wuhan; 430072, China

发表刊物：Future Generation Computer Systems

摘要：As development of social networks, social recommendation method, an effective information filtering technology, based on sociology rule and network theory, has improved performance of recommendation system and cold start problem. For data deep fusion and diverse development of social platform, the social relationship between users becomes more and more complex. The complexity of multiple social networks challenges social recommendation. However, most existing social recommendation methods focus on single social network, and multi-layer recommendation methods ignore nonlinearity and coupling between different social relationships. To tackle these problems, we propose a probabilistic matrix factorization model for multiply social networks joint recommendation framework based on joint probability distribution. This model analyzes different types of classic social networks and distribution function of user preferences similarity. Then we present unified model of recommendation based on social networks, as well as extensible multiply social networks joint recommendation method. The experimental results demonstrate comparing with relevant social recommendation algorithms; our method performs better on some evaluation indexes such as accuracy and errors. © 2018 Elsevier B.V.

合写作者： Shijun(1), Li, Wei(1),Yu

是否译文：否

发表时间：2018-01-01