关键词: 社会化标签, 推荐系统, 主题模型, 社会网络分析

Abstract: Social tagging systems which allow users to use personalized words to annotate resources are widely accepted by users. In micro-blog network, user can attach personalized labels to himself/herself so as to promote his/her recognition or facilitate others to find him/her. With the deep analysis of the micro-blog dataset, this paper summarizes the characteristics of user’s tag. Aiming at the shortcomings of LDA (latent Dirichlet allocation) topic model in dealing with short texts, this paper proposes a new relationship bind topic model. After that, the user’s topic distribution is calculated, and topic words are clustered. At last, tag recommendation is performed. The comparative experiments show that the proposed method can improve the accuracy and diversity of tag recommendation task.

Key words: social tagging, recommendation system, topic model, social network analysis