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It is a hot research topic to obtain user interest by analyzing user's UCC content. Identifying user interest changes in social networks is of great significance for providing high-quality personalized network services. For the cold - start' users with poor UGC, LDA topic model is not effective to obtain their interest, and it is more difficult to identify the interest changes of them. To solve this problem, LDA is firstly used to obtain network top ices, and by which the topic-tag model is constructed. Furthermore, the spatial vector similarity calculation method based on tag association is used to construct the user-topic model. Combined with the learning model, a user interest change recognition algorithm is proposed. The proposed algorithm is applied to the large-scale data set collected by the microblog website, and the network topic, the core users of the network topic and their probability distribution on the interest topic are obtained more accurately, thus the identification problem of user interest change in the model is solved. [ABSTRACT FROM AUTHOR]

The mining and analysis of user - generated content is an effective way to obtain microblog topic. Aiming at the problem of cold start due to the lack of user tags, this author proposes a microblog topic generation method by integrating user tags and topic tags. Firsdy, the user tag was used as the feature item to compress the sparse vector and calculate the similarity of user tag. Secondly, LDA topic model was used to extract the topic of users microblog by which the topic tag was generated. Thirdly, the two kinds of tags were integrated to establish the similarity model of user tag topic and obtain the topic of microblog. The proposed method was applied to the microb¬log data set and the microblog topic represented by hot tags was obtained. While it is basically consistent with the results gotten with LDA topic model, it is more representative [ABSTRACT FROM AUTHOR]