Automatically generating descriptions for resources by tag modeling.

We have been witnessing an increasing number of social tagging systems on the web. Tags help users understand a resource readily and accurately. In a social tagging system, however, there are typically a fairly large number of resources each associated with a long list of tags. When browsing resources, users are reluctant to read these tags one by one. Instead, users prefer a shorter list of tags as a compact description of a resource. Such a tag description facilitates users to understand the resource accurately and effortlessly. This calls for a generator for a tag description, which selects a set of high-quality tags for a given resource. The tag description condenses the original tag list by retaining the most important tags of the long list. We propose that a good generator should go beyond pure tag popularity and towards diversifying a tag description. In this paper, we present a general framework of selecting a set of k tags as the description for a given resource. In addition, a generative model BTM is proposed to model users' tagging process. The experimental results on real-world tagging data confirm the effectiveness of the proposed approach in social tagging systems, showing significant improvement over the other baselines.