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 tutorial, we will introduce Social Recommendation and elaborate on how the various characteristics and aspects are involved in the social platforms for collective intelligence. 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.

Dr. King's research interests include machine learning, web intelligence & social computing, and multimedia processing. In these research areas, he has over 200 technical publications in journals (JMLR, ACM TOIS, IEEE TNN, Neurocomputing, NN, IEEE BME, PR, IEEE SMC, JAMC, JASIST, IJPRAI, DSS, etc.) and conferences (NIPS, IJCAI, CIKM, SIGIR, KDD, PAKDD, ICDM, WWW, WI/IAT, WCCI, IJCNN, ICONIP, ICDAR, etc.). In addition, he has contributed over 20 book chapters and edited volumes. Moreover, Dr. King has over 30 research and applied grants. One notable system he has developed is the VeriGuide System, previously known as the CUPIDE (Chinese University Plagiarism IDentification Engine) 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.

Dr. King is an Associate Editor of the IEEE Transactions on Neural Networks (TNN) and IEEE Computational Intelligence Magazine (CIM). He is a member of the Editorial Board of the Open Information Systems Journal, Journal of Nonlinear Analysis and Applied Mathematics, and Neural Information Processing–Letters and Reviews Journal (NIP-LR). He has also served as Special Issue Guest Editor for Neurocomputing, International Journal of Intelligent Computing and Cybernetics (IJICC), Journal of Intelligent Information Systems (JIIS), and International Journal of Computational Intelligent Research (IJCIR). He is a senior member of IEEE and a member of ACM, International Neural Network Society (INNS), and Asian Pacific Neural Network Assembly (APNNA). Currently, he is serving the Neural Network Technical Committee (NNTC) and the Data Mining Technical Committee under the IEEE Computational Intelligence Society (formerly the IEEE Neural Network Society). He is also a member of the Board of Governors of INNS and a Vice-President and Governing Board Member of APNNA.