融合用户社会地位和矩阵分解的推荐算法

随着社交网络服务的日益流行，社交网络平台为推荐算法提供了丰富的额外信息.假设朋友之间共享更多的共同偏好并且用户往往易于接受来自朋友的推荐，越来越多的推荐系统利用社交网络中用户之间的信任关系来改进传统推荐算法的性能.然而，现有基于社交网络推荐算法忽略了2个问题：1)在不同的领域中，用户信任不同的朋友；2)由于用户在不同的领域内具有不同的社会地位，因此，用户在不同的领域内受朋友的影响程度是不同的.首先利用整体的社交网络结构信息和用户的评分信息推导特定领域社交网络结构，然后利用PageRank算法计算用户在特定领域的社会地位，最后提出了一种融合用户社会地位信息的矩阵分解推荐算法.在真实数据集上的实验结果表明：融合用户地位信息的矩阵分解推荐算法的性能优于传统的基于社交网络推荐算法.

Integrating User Social Status and Matrix Factorization for Item Recommendation

With the increasing popularity of online social network services, social networks platforms provide rich information for recommender systems. Based on the assumption that friends share more common interests than non-friends and users tend to accept the item recommendations from friends, more and more recommender systems utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of the existing social-network-based recommendation algorithms ignore the following problems: 1) in different domains, users tend to trust different friends; 2) the degree of influence that a user is affected by their trusted friends is different in different domains since the user has different social status in different domains. In this paper, we first infer domain-specific social trust relation networks based on original users’ rating information and social network information, and then compute each user’s social status by leveraging PageRank algorithm for each specific domain. Finally, we propose a novel recommendation algorithm by integrating users’ social status with matrix factorization model. Experimental results on real-world dataset show that our proposed approach outperforms traditional social-network-based recommenda-tion algorithms.