结合矩阵分解的混合型社会化推荐算法

推荐系统是用来解决当今时代信息过载的重要工具。随着在线社交网络的出现和普及，一些基于网络推荐算法研究的出现，已经引起研究者的广泛关注。信任是社会网络中的重要信息之一，通常用来改进基于社交网络的推荐系统，然而，大多数信任感知的推荐系统忽略了用户有不同行为偏好在不同的兴趣域；本文不仅考虑了用户间特定域信任网络，并且结合推荐项目之间特征属性信息，提出了一种新型社会化推荐算法(H-PMF)。实验表明，H-PMF算法在评分误差和推荐精度上都取得了更好的效果。

A Hybrid Socialized Recommendation Algorithm Based on Matrix Factorization

Recommender systems (RSs) have become important tools for solving the problem of information overload. With the emergence and popularity of online social networks, some studies on network-based recommendation algorithm have emerged, raising the concern of many researchers. Trust is one kind of important information available in social networks and is often used for performance improvement in social-network-based RSs. However, most trust-aware RSs ignore the fact that the user has different preference in different domains of interest. In this paper, we propose a new social recommendation algorithm (H-PMF), which not only considers the user-specific domain trust network, but also combines the feature attribute information between recommended items. Experiments show that the H-PMF algorithm has better performance in both scoring error and recommendation accuracy.