关键词: 个性化推荐, 标签, TF-IDF, 基于网络的推荐

Abstract: The structure between user and item is only considered in the network-based inference algorithm regardless of personalized preferences, collaborative tags contain rich information about personalized preferences and item contents, and then a hybrid recommendation method is proposed based on network and tag. In this paper, personalized preferences is constructed according to the method of TF-IDF and the tag support, and a linear combination of recommendation model is presented by merging network-based inference and personalized preferences. The benchmark data set, MovieLens, is used to evaluate the algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and personalized of recommendations.

Key words: personalized recommendation, tag, TF-IDF, network-based inference