GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Mining long-term search history to improve search accuracy
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
Evaluating the Effectiveness of Personalized Web Search
IEEE Transactions on Knowledge and Data Engineering
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
User profiles for personalized information access
The adaptive web
Finding Overlapping Communities in Social Networks
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
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The social network represents various relationships between users, and community discovery is one of the most popular tasks analyzing these relationships. The relationships are either explicit (e.g., friends) or implicit, and we focus on community discovery with implicit relationships. Here, the key issue is how to extract the relationships between users. A user is typically represented by his/her profile, and the similarity between user profiles is measured. In most algorithms, a user has a single profile aggregating all the information about the user. For example, a profile for a researcher is a list of papers he/she wrote. This setting, however, oversimplifies the multiple characteristics of a man since individual characteristics are mixed up. In this paper, we propose the notion and method of profile decomposition, which divides a profile into a set of sub-profiles so that they represent individual characteristics precisely. Then, we develop a community discovery algorithm, which we call DecompClus, based on profile decomposition. Using a real data set of CiteULike, we show that our proposed algorithm can precisely distinguish multiple research interests of a user and discover communities corresponding to each interest, whereas previous algorithms cannot. Overall, profile decomposition enables us to find fine-granularity user communities, thus improving the accuracy of community discovery.