Guiding supervised topic modeling for content based tag recommendation

Automatically recommending suitable tags for online content is a necessary task for better information organization and retrieval. In this article, we propose a generative model SIMWORD for the tag recommendation problem on textual content. The key observation of our model is that the tags and their relevant/similar words may have appeared in the corresponding content. In particular, we first empirically verify this observation in real data sets, and then design a supervised topic model which is guided by the above observation for tag recommendation. Experimental evaluations demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy. (C) 2018 Elsevier B.V. All rights reserved.