]> BibSonomy publications for /author/Guo/community http://www.bibsonomy.org/burst/author/Guo/community BibSonomy BuRST Feed for /author/Guo/community 2008-10-08T11:19:26+02:00 Tag-based Social Interest Discovery http://www.bibsonomy.org/bibtex/242b4c94cff05ccef031235d661a7a77a/hotho hotho 2008-05-09T12:06:13+02:00 *** association clustering community del.icio.us detection folksonomy rules
Tag-based Social Interest Discovery
Xin Li and Lei Guo and Yihong E. Zhao Proceedings of the 17th International World Wide Web Conference 675-684 (2008)
to *** association clustering community del.icio.us detection folksonomy rules by hotho on 2008-05-09 12:06:13 ]]> Proceedings of the 17th International World Wide Web Conference675-684Tag-based Social Interest Discovery2008*** association clustering community del.icio.us detection folksonomy rules 2008-05-09 12:06:13.0The success and popularity of social network systems, such as del.icio.us, Facebook, MySpace, and YouTube, have generated many interesting and challenging problems to the research community. Among others, discovering social interests shared by groups of users is very important because it helps to connect people with common interests and encourages people to contribute and share more contents. The main challenge to solving this problem comes from the diffi- culty of detecting and representing the interest of the users. The existing approaches are all based on the online connections of users and so unable to identify the common interest of users who have no online connections. In this paper, we propose a novel social interest discovery approach based on user-generated tags. Our approach is motivated by the key observation that in a social network, human users tend to use descriptive tags to annotate the contents that they are interested in. Our analysis on a large amount of real-world traces reveals that in general, user-generated tags are consistent with the web content they are attached to, while more concise and closer to the understanding and judgments of human users about the content. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. We have developed an Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics. Our evaluation shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections.