基于多子网复合复杂网络模型的物质扩散推荐算法

Mass Diffusion Recommendation Algorithm Based on Multi-Subnet Composited Complex Network Model

融合社交网络的社会化推荐算法是目前推荐系统中普遍采用的方法。在现实的社交网络中,用户间存在多种关系,而每种关系对于推荐的影响是不同的,因此在推荐中单纯引入某一种社交关系必然影响推荐结果的准确率。本文基于多子网复合复杂网络模型,通过在用户-商品二部图上加载多关系社交网络,构建多关系复合网,提出了基于多关系复合网的物质扩散推荐算法。在真实的数据集Epinions和FilmTrust上的实验结果表明,加入两种社交关系的推荐算法比加入一种社交关系的推荐算法及传统的物质扩散算法在推荐准确率方面有显著提高。

Social recommendation algorithm based on social network is a popular method in recommendation system at present. However, there are many relationships among users in real social networks, and each relationship has different effects on recommendation. Therefore, the introduction of a social relationship in recommendation will inevitably affect the accuracy of recommendation results. In this paper, based on the multi-subnet composited complex network model, a multi-relationship composited network is constructed on the user-item bipartite graph, and a mass diffusion recommendation algorithm based on multi-relationship composited network is proposed. The experimental results on the real datasets Epinions and FilmTrust show that the recommendation algorithm with two kinds of social relationships is better than the recommendation algorithm with one kind of social relationship and traditional mass diffusion algorithm in the accuracy of recommendation.