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Creators/Authors contains: "Xi, Shengqu"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security)
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  2. ; ; ; ; ; (, Neurocomputing)
    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. 
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