%0 Journal Article
%A SUN Jia-shen
%A WANG Xiao-jie
%T A Topic Model for Social Tag Recommendation
%D 2014
%R 10.13190/j.jbupt.2014.03.008
%J Journal of Beijing University of Posts and Telecommunications
%P 38-42
%V 37
%N 3
%X <p>It is common that the topic-granularity of social tags is not consistent with correspondent document, and some tags cannot describe the topic of the document content. The existing topic models-based tag recommendation did not address the foregoing problems simultaneously as well. Motivated by the fact, the proposed novel topic model allows different granularity of word topics and tag topics, and assumes that the tags can originate from a general distribution unrelated to the content. Experimental results show that the proposed model outperforms content relevance model (CRM) and tag- logical device address (tag-LDA) on two different social tagging corpora in both perplexity and mean average precision.</p>
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2014.03.008