Tag Ranking

Presented at: 18th International World Wide Web Conference (WWW2009)

by Dong Liu, Xian-Sheng Hua, Linjun Yang, Meng Wang, Hong-Jiang Zhang

Webpage: http://www2009.eprints.org/36/1/p351.pdf

Social media sharing web sites like Flickr allow users to annotate images with free tags, which significantly facilitate Web image search and organization. However, the tags associated with an image generally are in a random order without any importance or relevance information, which limits the effectiveness of these tags in search and other applications. In this paper, we propose a tag ranking scheme, aiming to automatically rank the tags associated with a given image according to their relevance to the image content. We first estimate initial relevance scores for the tags based on probability density estimation, and then perform a random walk over a tag similarity graph to refine the relevance scores. Experimental results on a 50, 000 Flickr photo collection show that the proposed tag ranking method is both effective and efficient. We also apply tag ranking into three applications: (1) tag-based image search, (2) tag recommendation, and (3) group recommendation, which demonstrates that the proposed tag ranking approach really boosts the performances of social-tagging related applications.

Keywords: Rich Media


Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2009/paper/36


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