Tag-based social interest discovery

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.

Personalization of tagging systems

“…Previous studies have already shown that tags cannot only improve the search effectiveness (Heymann, Koutrika, & Garcia-Molina, 2008;Xu, Bao, Fei, Su, & Yu, 2008), but also support knowledge discovery (Li, Guo, & Zhao, 2008). Schenkel et al (2008) rank top-k results looking at social and semantic dimensions.…”

Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information

“…This latter considers the tags as the users interests [9,23]. We compare the result provided by our approach with the result of the approach that uses all the tags of the neighbours (without considering their relevance to the associated resources).…”

Privacy through Web-Traveler Policies in Social Network Environments

“…While their mechanism develops a logical context data model and a physical data store, our mechanism deals with the concrete challenges of making such type of protection feasible, without relying on third parties' verification or auditing. Concerning policy propagation based on folksonomies, our work builds on related approaches on customization and personalization of tag-based information retrieval [12], [19], [14]. Several techniques involved in exploring social annotations include association rule mining [14] and EM-based probabilistic learning approach [12], [19], [24].…”

“…Concerning policy propagation based on folksonomies, our work builds on related approaches on customization and personalization of tag-based information retrieval [12], [19], [14]. Several techniques involved in exploring social annotations include association rule mining [14] and EM-based probabilistic learning approach [12], [19], [24].…”