The Analysis of User Tags Recommended Model Based on Selection Association Rule

Social networking allows users to personalize the vocabulary of network resources. Therefore, social networks have become a representative scenario of social tagging systems. In micro-blogging networks, users can personalize their own labels to show the individual social attributes. This paper analyzes the micro-blog user data, summarizes the characteristics of the micro-blog user tags, and for the lack of personalized labels for existing models used in microblogging scene, we propose an improved association rule model based on the recommendation of the selection. On this basis, the microblogging users selected tags is analyzed, the correlation of user labels is calculated, and finally associated personalized labels are recommended to users. The model can be improved determined while ensuring the diversity of recommendations by comparing the experimental results.