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Topic-Sensitive Tag Ranking

10 years 6 months ago
Topic-Sensitive Tag Ranking
Social tagging is an increasingly popular way to describe and classify documents on the web. However, the quality of the tags varies considerably since the tags are authored freely. How to rate the tags becomes an important issue. In this paper, we propose a topic-sensitive tag ranking (TSTR) approach to rate the tags on the web. We employ a generative probabilistic model to associate each tag with a distribution of topics. Then we construct a tag graph according to the co-tag relationships and perform a topic-level random walk over the graph to suggest a ranking score for each tag at different topics. Experimental results validate the effectiveness of the proposed tag ranking approach.
Yan'An Jin, Ruixuan Li, Zhengding Lu, Kunmei Wen,
Added 30 Sep 2010
Updated 30 Sep 2010
Type Conference
Year 2010
Where ICPR
Authors Yan'An Jin, Ruixuan Li, Zhengding Lu, Kunmei Wen, Xiwu Gu
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