Abstract: In order to improve the performance of personalized tag recommendation method, the Gibbs sampling inference based RTM model for Micro-blog personalized label recommendation was proposed. Firstly, we used the user topic distribution as the representative of the user with Top-k similar for the potential local information in micro-blog, Then we calculated the frequency of all tags appearing in these users, and recommended the most relevant tags with the user. Secondly, in order to explore the potential theme information, we named the micro-blog tags by using the implicit relation model, which could greatly improves the influence of the joint modeling on the latent topic generation tags, and finds out the relationship between the global tags and the topic; Finally, the experimental results show that the proposed method is superior to the selected TF-IDF, RTMSA and other classical label recommendation algorithm, and verify the effectiveness of the proposed algorithm.