CIS Colloquium, Mar 04, 2011, 11:00AM - 12:00PM, Wachman 447
Towards the Next Generation of Mobile Recommender Systems
Hui Xiong, Rutgers University
Recommender systems aim to identify content of interest from overloaded information by exploiting the opinions of a community of users. Developing personalized recommender systems in mobile and pervasive environments is more challenging than developing recommender systems from traditional domains due to the complexity of spatial data, the unclear roles of context-aware information, and the increasing availability of environment- sensing capabilities. In this talk, we introduce the unique features that distinguish pervasive personalized recommendation systems from classic recommendation systems. An examination of major research needs in pervasive personalized recommendation research reveals some new opportunities for personalized recommendation in mobile and pervasive applications.
Dr. Hui Xiong received his Ph.D. from the University of Minnesota and the B.E degree from the University of Science and Technology of China (USTC). He is currently an Associate Professor at Rutgers University, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), an IBM ESA Innovation Award (2008), the Junior Faculty Teaching Excellence Award (2007) and the Junior Faculty Research Award (2008) at the Rutgers Business School. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive business applications. He is an Associate Editor of the Knowledge and Information Systems journal. He has served regularly in the organization committees and the program committees of a number of international conferences and workshops. More detailed information is available at http://datamining.rutgers.edu.