NDLI: Automatically generating descriptions for resources by tag modeling

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Automatically generating descriptions for resources by tag modeling

Content Provider	ACM Digital Library
Author	Cho, Junghoo Bi, Bin
Abstract	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.
Starting Page	2387
Ending Page	2392
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505632
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Tagging system Tag generation Generative model
Content Type	Text
Resource Type	Article

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