Social recommendation algorithm is a common tool for recommending interesting or potentially useful items to users amidst the sea of online information. The users usually have various relationships, each of which has its unique impact on the recommendation results. It is unlikely to make accurate recommendations solely based on one relationship. Based on user-item bipartite graph, this paper establishes a multisubnet composited complex network (MSCCN) of multiple user relationships and then extends the mass diffusion (MD) algorithm into a novel intelligent recommendation algorithm. Two public online datasets, namely, Epinions and FilmTrust, were selected to verify the effect of the proposed algorithm. The results show that the proposed intelligent recommendation algorithm with two types of relationships made much more accurate recommendations than that with a single relationship and the traditional MD algorithm.

中文翻译：

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一种基于质量扩散的新型智能推荐算法

社交推荐算法是在在线信息海中向用户推荐有趣或潜在有用项目的常用工具。用户通常具有各种关系，每种关系对推荐结果都有其独特的影响。仅基于一种关系不可能提出准确的建议。基于用户项二部图，建立了具有多个用户关系的多子网复合复杂网络（MSCCN），然后将质量扩散（MD）算法扩展为一种新颖的智能推荐算法。选择了两个公共的在线数据集Epinions和FilmTrust来验证所提出算法的效果。