融合社交网络和兴趣的正则化矩阵分解推荐模型

Regularized matrix decomposition recommendation model integrating social networks and interest correlation

针对目前用户偏好数据和社交关系数据十分稀疏的问题,以及用户可能更加喜欢朋友推荐的商品而不喜欢非朋友推荐的商品这样一个事实,提出了一种结合社交网络和用户间的兴趣偏好相似度的正则化矩阵分解推荐算法,首先针对社交关系数据稀疏问题,利用网络的全局和局部拓扑特性挖掘出用户间的信任和不信任关系矩阵,然后定义了一种改进的用户间的兴趣偏好相似度计算方法,最后在矩阵分解的过程中将信任矩阵、不信任矩阵以及兴趣相关性综合起来为用户作出推荐。实验表明该方法优于主要的正则化推荐方法,与基本的矩阵分解模型(Social MF)、SoRec、Trust MF、CTRPMF、Rec SSN算法相比,算法在均方根误差(RMSE)和平均绝对误差(MAE)上分别减小了1.1%~9.5%和2%~10.1%,取得了较好的推荐效果。

In view of the fact that users preferences and social interaction data are very sparse,and the fact that users may prefer products recommended by friends than recommended by foes,a regularized matrix decomposition recommendation algorithm integrating with social network and interest preference similarity was proposed.First of all,for the problem of sparse data of social relations.Global and local topological characteristics of the network were used to extract trust and distrust matrices between users respectively.Secondly,a method for calculating interest preference similarity between users was defined.Finally,in the process of matrix decomposition,the trust matrix,the distrust matrix,and the interest correlation were synthetically taken into consideration to make recommendations for the users.Experiments show that this method is superior to other regularization recommendation methods.Compared with the basic matrix decomposition model(SocialMF),SoRec,TrustMF,CTRPMF and RecSSN algorithm,the proposed algorithm reduces 1.1%to 9.5%and 2%to 10.1%respectively in the root mean square error(RMSE)and the mean absolute error(MAE),improved recommendations effectively.