%0 Journal Article %1 sun2013topical %A Sun, Xiaoling %A Lin, Hongfei %D 2013 %J Journal of the American Society for Information Science and Technology %K community detection generative models tagging topic %P n/a--n/a %R 10.1002/asi.22740 %T Topical community detection from mining user tagging behavior and interest %U http://dx.doi.org/10.1002/asi.22740 %X With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.

@article{sun2013topical, abstract = {With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.}, added-at = {2013-01-05T12:03:24.000+0100}, author = {Sun, Xiaoling and Lin, Hongfei}, biburl = {https://www.bibsonomy.org/bibtex/239b8b4528ae729dea5c76de05cf8f70b/folke}, description = {Topical community detection from mining user tagging behavior and interest - Sun - 2012 - Journal of the American Society for Information Science and Technology - Wiley Online Library}, doi = {10.1002/asi.22740}, interhash = {24952b8a6f860ac669b9e985dcb0c0b1}, intrahash = {39b8b4528ae729dea5c76de05cf8f70b}, issn = {1532-2890}, journal = {Journal of the American Society for Information Science and Technology}, keywords = {community detection generative models tagging topic}, pages = {n/a--n/a}, timestamp = {2013-01-05T12:03:24.000+0100}, title = {Topical community detection from mining user tagging behavior and interest}, url = {http://dx.doi.org/10.1002/asi.22740}, year = 2013 }