基于多维信任和联合矩阵分解的社会化推荐方法

Social recommendation method based on multi-dimensional trust and collective matrix factorization

针对现有社会化推荐算法在信任分析方面的不足,研究了从社交辅助信息中充分挖掘用户信任关系的方法,进而提出一种基于多维信任计算和联合矩阵分解的社会化推荐算法。首先,从用户社交行为、社交圈特征获得用户的动态和静态两种局部信任度,再利用信任网络的结构特征提取全局信任度;然后,构造一种对增强关注矩阵和社交信任矩阵进行联合矩阵分解的社会化推荐算法,并采用随机梯度下降法对其求解。基于新浪微博数据集的实验结果表明,所提出的算法在推荐精度和Top-K推荐能力方面明显优于socailMF、 LOCABAL、 contextMF和TBSVD这几种代表性的社会化推荐算法。

Aiming at the shortages in trust analysis of existing social recommendation algorithms, a social recommendation algorithm based on multi-dimensional trust and collective matrix factorization was proposed with full use of user trust relationship mined from social auxiliary information. Firstly, the dynamic and static local trust relationships were extracted respectively from social interaction behaviors and social circle features of the user, and the global trust relationship was extracted from the structural features of trust network. Then, a social recommendation algorithm was presented by collective factorizing the enhanced following relationship matrix and the social trust relationship matrix, and a stochastic gradient descent method was utilized to solve the algorithm. The experimental results on the Sina microblog dataset indicate that the proposed algorithm outperforms some popular social recommendation algorithms such as socialMF, LOCABAL, contextMF and TBSVD(Trust Based Singular Value Decomposition), in terms of recommendation accuracy and Top-K performance.