Abstract. 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.

Short-Biography

Dr. Hui Xiong is currently an Associate Professor and the Vice Chair of the
Management Science and Information Systems Department at the Rutgers, the
State University of New Jersey, where he received  a two-year early
promotion/tenure (2009),  the Rutgers University  Board of Trustees Research
Fellowship for Scholarly Excellence (2009), the ICDM-2011 Best Research
Paper Award (2011), the Junior Faculty Teaching Excellence Award (2007) and
the Junior Faculty Research Award (2008) at Rutgers Business School. Dr.
Xiong received his Ph.D. in Computer Science from the University of
Minnesota (UMN), USA, in 2005, the B.E. degree in Automation from the
University of Science and Technology of China (USTC), Hefei, China, and the
M.S. degree in Computer Science from the National University of Singapore
(NUS), Singapore. 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 applications. He has
published prolifically in refereed journals and conference proceedings (3
books, 40+ journal papers, and 60+ conference papers). He is the
co-Editor-in-Chief of Encyclopedia of GIS (Springer, 2007) and an Associate
Editor of the Knowledge and Information Systems journal. He has served
regularly on the organization and program committees of numerous
conferences, including as a Program Co-Chair of the Industrial and
Government Track for the 18th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD). He is a senior member of the ACM
and the IEEE.