关键词: 推荐算法, 用户标签, 标签基因, TF-IDF, 实值条件受限玻尔兹曼机（R_CRBM）

Abstract: To solve data sparseness problem of recommendation algorithm, this paper fuses the tag to the conditional restricted Boltzmann machines model. It utilizes CRBM??s powerful ability to fit arbitrary discrete distribution to predict the missing scores for the user unevaluated products. Specifically, it proposes the real-valued conditional restricted Boltzmann machine model whose visible units are real value firstly. Then, the TF-IDF algorithm of text classification is used to predict the attitude of the user who applies the tags, which is multiplied by the tag-genome to get the user??s scores for products, which are integrated into the user history rating data. In the conditional layer, this paper incorporates user tagged/untagged {0, 1} vector in the original rated/unrated {0, 1} vector. Finally, the comparative experimental analysis about real-world dataset shows that the proposed method enhances the accuracy of recommendation.

Key words: recommendation algorithm, tag, tag-genome, term frequency-inverse document frequency (TF-IDF), real-valued conditional restricted Boltzmann machine (R_CRBM)