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传统的推荐系统只使用用户的评分信息进行计算并进行推荐,虽然在一定程度上能够获得用户或资源的隐含特征,但缺乏足够的语义解释,影响了推荐效果.针对此问题,提出了一种融合社会标签的近邻感知的联合概率矩阵分解推荐算法.首先,该算法通过标签的相似性来计算用户间和资源之间的相似性,进行近邻选择;其次,构建用户-资源评分矩阵、用户-标签标注矩阵、资源-标签关联矩阵并运用联合概率矩阵分解方法计算3个矩阵的隐含特征向量,通过对训练模型进行参数优化,为用户进行推荐.实验结果表明,该算法可以有效利用标签的语义性,提高推荐质量.“,”Traditional recommendation systems only use the users′ rating information for the calculation and the recommendation.We can obtain the latent feature of the users or the resources to some extent but cannot get enough semantic interpretation which affects recommendation results.In order to solve this problem, we propose a neighborhood-aware unified probabilistic matrix factorization recommendation algorithm which combines social tags.First, we calculate the similarity between the users and the resources through the similarity of the tags to make neighborhood selection.Second, we construct a user-resource rating matrix, a user-tag tagging matrix and a resources-tag correlation matrix, and use the unified probability matrix factorization to get the latent feature vectors of three matrices to recommend by optimizing training model parameter.The experimental results show that the proposed algorithm can effectively use the semantics of the tags and improve the recommendation quality.