Prof. Irwin King's research interests include machine learning, social computing, web intelligence, data mining, big data, and multimedia information processing. In these research areas, he has over 200 technical publications in journals and conferences. In addition, he has contributed over 30 book chapters and edited volumes. Prof. King is the Book Series Editor for ?Social Media and Social Computing? with Taylor and Francis (CRC Press). He is also an Associate Editor of the ACM Transactions on Knowledge Discovery from Data (ACM TKDD), Journal of Neural Networks, and a former Associate Editor of the IEEE Transactions on Neural Networks (TNN). He is a member of a number of Editorial Boards. He is also a member of the Board of Governors of INNS and a Vice-President and Governing Board Member of APNNA. He also serves INNS as the Vice-President for Membership in the Board of Governors.

Prof. King is Associate Dean (Education) at the Engineering Faculty and Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He is also the Director of the Shenzhen Key Lab on Rich Media and Big Data, SZRI. Recently, he was on leave with AT&T Labs Research, San Francisco and also taught Social Computing and Data Mining as a Visiting Professor at UC 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.

The Big Data Era has ushered in a new wave of research that investigates how we can better handle data with characteristics such as high volume, velocity, veracity, and variety. Social Computing examines the collective intelligent behavior resulted from interactions among social entities. In the first part of the talk, I plan to draw some observations on the interplay between Social Computing and Big Data. I will then focus on our recent work on social and location recommendations based on matrix factorization framework as a case study that demonstrates how filtered suggestions are highly desirable to cope with the information explosion problem. I will outline novel ways on how we can use social ensemble, trust relations, tags, click-through-rate, etc. to improve social and location recommender systems for a wide-range of applications and services in the era of Big Data.