Abstract
In order to improve performance of personalized label recommendation, the recommendation algorithm of implied relation topic model (RTM) for microblog personalized label based on Gibbs sampling inference is proposed. Firstly, imaging form is used to express potential local information in microblog and to conduct top-k similar user discovery for users represented as user topic distribution, and then the frequency of all labels in these users is calculated to recommend the label mostly related to the users. Secondly, to dig potential topic information, enhancement cosine similarity RTM model with penalty term is used to name the microblog label, which greatly improves the influence of joint modeling on potential topic generation label, and the relationship between overall label and topic can be found; finally, it can be seen from real experimental result that the proposed recommendation method is superior to the selected TF–IDF, RTMSA and other classic label recommendation algorithm, so as to verify effectiveness of the algorithm.
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