ABSTRACT
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.
- R. Agrawal, T. Imieliński, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of ACM SIGMOD, pages 207--216, June 1993. Google ScholarDigital Library
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. VLDB, pages 487--499, Sept. 1994. Google ScholarDigital Library
- N. Ali-Hasan and L. Adamic. Expressing social relationships on the blog through links and comments. In Proc. of International Conference on Weblogs and Social Media, Mar. 2007.Google Scholar
- S. Bateman, C. Brooks, G. McCalla, and P. Brusilovsky. Applying collaborative tagging to e-learning. In Proc. of ACM WWW, May 2007.Google Scholar
- L. Breslau, P. Cao, L. Fan, G. Philips, and S. Shenker. Web caching and Zipf-like distributions: Evidence and implications. In Proc. of INFOCOM, Mar. 1999.Google ScholarCross Ref
- C. H. Brooks and N. Montanez. Improved annotation of blogosphere via autotagging and hierarchical clustering. In Proc. of ACM WWW, pages 625--631, May 2006. Google ScholarDigital Library
- A. Clauset, M. E. J. Newman, and C. Moore. Finding community structure in very large networks. Physical Review E, 70(066111), 2004.Google Scholar
- S. A. Golder and B. A. Huberman. Usage patterns of collaborative tagging system. Journal of Information Science, 32(2):198--208, 2006. Google ScholarDigital Library
- L. Guo, S. Jiang, L. Xiao, and X. Zhang. Fast and low-cost search schemes by exploiting localities in p2p networks. Journal of Parallel and Distributed Computing, 65(6):729--742, 2005. Google ScholarDigital Library
- H. Halpin, V. Robu, and H. Shepherd. The complex dynamics of collaborative tagging. In Proc. of ACM WWW, pages 211--220, May 2007. Google ScholarDigital Library
- H. Kautz, B. Selman, and M. Shah. Referral Web: combining social networks and collaborative filtering. Communications of the ACM, 40(3):63--65, 1997. Google ScholarDigital Library
- K. Lerman, A. Plangrasopchok, and C. Wong. Personalizing results of image search on flickr. In AAAI workshop on Intelligent Techniques for Web Personlization, 2007.Google Scholar
- A. Plangprasopchok and K. Lerman. Exploiting social annotation for automatic resource discovery. In AAAI workshop on Information Integration from the Web, 2007.Google Scholar
- M. F. Schwartz and D. C. M. Wood. Discovering shared interests using graph analysis. Communications of the ACM, 36(8):78--89, 1993. Google ScholarDigital Library
- K. Sripanidkulchai, B. Maggs, and H. Zhang. Efficient content location using interest-based locality in peer-to-peer systems. In Proc. of INFOCOMM, Mar. 2003.Google ScholarCross Ref
- X. Wu, L. Zhang, and Y. Yu. Exploring social annotations for the semantic web. In WWW ?06: Proceedings of the 15th international conference on World Wide Web, pages 417--426, New York, NY, USA, 2006. ACM. Google ScholarDigital Library
Index Terms
- Tag-based social interest discovery
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