关键词:  , 矩阵分解技术； 社会化推荐； 特征空间模型； 社会信息

Abstract: Social network includes vast amount of social information, how to find information users are interested in has become research focus of many scholars and experts. Based on this idea, this paper proposes a social regularization-based recommendation algorithm: apply the matrix factorization technology into the social recommendation, make use of friendship between users to get better user’s feature space, consider the tag information in social network, and use this information to learn better user and item feature space. The analysis of experiments shows that the accuracy of the improved algorithm is better than the traditional recommendation algorithm and it solves the problem of redundant social information effectively.

Key words: matrix factorization technology, social recommendation, feature space model, social information