Extracting Representative Tags for Flickr Users

Tags are very popular in online social communities (like You tube, Flickr) and provide valuable and crucial information for these communities. But at the same time, there exist a lot of noisy tags, which leads many researches to tag suggestion, tag recommendation for the items, such as to the websites, photos, books, movies, and so on. Most of them used the textural features of tags to extract related tags to items, like tag frequency. In our paper, we address the problem of tag recommendation for users in Flickr. This issue is as important as tag recommendation for items, because representative tags of users are strongly related to users' favorite topics. We propose several novel features of tags which we call them social features as well as textual features. Experimental results show that our proposed scheme achieves viable performance on tag recommendation for users.