Computational Aspects of Social and Information Networks (CASIN2011)

Models and Techniques for Social Recommendation

Irwin King

AT&T Labs Research
San Francisco, CA USA

Department of Computer Science and Engineering
The Chinese University of Hong Kong


As the exponential growth of information generated on the World Wide Web, Social Recommendation has emerged as one of the hot research topics recently. Social Recommendation forms a specific type of information filtering technique that attempts to suggest information (blogs, news, music, travel plans, web pages, images, tags, etc.) that are likely to interest the users. Social Recommendation involves the investigation of collective intelligence by using computational techniques such as machine learning, data mining, natural language processing, etc. on social behavior data collected from blogs, wikis, recommender systems, question & answer communities, query logs, tags, etc. from areas such as social networks, social search, social media, social bookmarks, social news, social knowledge sharing, and social games. In this talk, we plan to elaborate on the various characteristics and aspects that are involved in Social Recommendation. Moreover, we will discuss the challenging issues involved in Social Recommendation in the context of theory and models of social networks, methods to improve recommender systems using social contextual information, ways to deal with partial and incomplete information in the social context, scalability and algorithmic issues with social computational techniques.

Brief Profile

Irwin King
Irwin King's research interests include machine learning, web intelligence & social computing, and multimedia processing. In these research areas, he has over 220 technical publications in journals and conferences. In addition, he has contributed over 20 book chapters and edited volumes. Moreover, Irwin has over 30 research and applied grants. One notable system he has developed is the VeriGuide System, which detects similar sentences and performs readability analysis of text-based documents in both English and in Chinese to promote academic integrity and honesty. He has served as reviewer and panel member for RGC Hong Kong, Natural Sciences and Engineering Research Council of Canada (NSERC), National Natural Science Foundation of China (NSFC), and Natural Science, and Engineering of Academy of Finland. Irwin is an Associate Editor of the IEEE Transactions on Neural Networks (TNN). He is a member of the Editorial Board and Special Issue Editor of a number of international journals. He is also a member of the Board of Governors and Vice-President of Membership of INNS and a Vice-President and Governing Board Member of APNNA. He is Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He is currently on leave with AT&T Labs Research, San Francisco and also a Visiting Professor at UC Berkeley, Berkeley. He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles. See http://www.cse.cuhk.edu.hk/irwin.king/ for more information.

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