document: Tag-based social interest discovery The 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 difficulty 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 .
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