Topic-sensitive Tag Ranking
出版社：
摘要内容：
Social tagging is an increasingly popular way to de-scribe and classify documents on the web. However, thequality of the tags varies considerably since the tags areauthored freely. How to rate the tags becomes an impor-tant issue. In this paper, we propose a topic-sensitivetag ranking (TSTR) approach to rate the tags on theweb. We employ a generative probabilistic model to as-sociate each tag with a distribution of topics. Then weconstruct a tag graph according to the co-tag relation-ships and perform a topic-level random walk over thegraph to suggest a ranking score for each tag at differ-ent topics. Experimental results validate the effective-ness of the proposed tag ranking approach.
摘要内容：
Social tagging is an increasingly popular way to de- scribe 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 impor- tant 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 as- sociate each tag with a distribution of topics. Then we construct a tag graph according to the co-tag relation- ships and perform a topic-level random walk over the graph to suggest a ranking score for each tag at differ- ent topics. Experimental results validate the effective- ness of the proposed tag ranking approach.
