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Probabilistic Matrix Factorization Recommendation Algorithm with User Trust Similarity

机译:具有用户信任相似度的概率矩阵分解推荐算法

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In this paper, we describe the formatting guidelines for Conference Proceedings. Whether the user similarity calculation is reasonable in the traditional collaborative filtering recommendation algorithm directly affects the result of the collaborative filtering recommendation algorithm. This paper proposes a probabilistic matrix factorization recommendation algorithm with user trust similarity which combines improved similarity of users’ trust and probability matrix factorization recommendation method. The results show that proposed algorithm could relieve user cold start issues and effectively reduce the error of recommendation.
机译:在本文中,我们描述了会议录的格式指南。传统协同过滤推荐算法中用户相似度计算是否合理直接影响了协同过滤推荐算法的结果。提出了一种具有用户信任相似度的概率矩阵分解推荐算法,该算法结合了改进的用户信任相似度和概率矩阵分解推荐方法。结果表明,该算法可以缓解用户冷启动问题,有效减少推荐错误。

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