%0 Conference Paper %1 liu2009ranking %A Liu, Dong %A Hua, Xian-Sheng %A Yang, Linjun %A Wang, Meng %A Zhang, Hong-Jiang %B Proceedings of the 18th international conference on World wide web %C New York, NY, USA %D 2009 %I ACM %K example_simi flickr pagerank tag %P 351--360 %R 10.1145/1526709.1526757 %T Tag ranking %U http://doi.acm.org/10.1145/1526709.1526757 %X 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</p> <p>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. %@ 978-1-60558-487-4

@inproceedings{liu2009ranking, abstract = {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</p> <p>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.}, acmid = {1526757}, added-at = {2014-02-28T08:20:02.000+0100}, address = {New York, NY, USA}, author = {Liu, Dong and Hua, Xian-Sheng and Yang, Linjun and Wang, Meng and Zhang, Hong-Jiang}, biburl = {https://www.bibsonomy.org/bibtex/2ebe29b5c3ed7a2f70b8f4078be0d0aad/inmantang}, booktitle = {Proceedings of the 18th international conference on World wide web}, description = {Tag ranking}, doi = {10.1145/1526709.1526757}, interhash = {252b9dbe98e7bba1d77be07b109d109c}, intrahash = {ebe29b5c3ed7a2f70b8f4078be0d0aad}, isbn = {978-1-60558-487-4}, keywords = {example_simi flickr pagerank tag}, location = {Madrid, Spain}, numpages = {10}, pages = {351--360}, publisher = {ACM}, series = {WWW '09}, timestamp = {2014-02-28T08:20:02.000+0100}, title = {Tag ranking}, url = {http://doi.acm.org/10.1145/1526709.1526757}, year = 2009 }