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    What’s Special about Mobile Recommender Systems?
    Update time: 2012-04-25
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    Time:9:30 am, Apr. 27, 2012

    Place:Room 4, ICT. CAS


    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 is 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), and the ICDM-2011 Best Research Paper Award. His general research area is data and knowledge engineering, focusing 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 and an Associate Editor of the Knowledge and Information Systems journal.


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