BIG DATA SEMINAR SERIES, May 11, 2016, 10:00AM – 11:00AM, Alter Hall 505
PERSONALIZED RECOMMENDATIONS IN MOBILE BUSINESS ENVIRONMENTS
Bin Liu , DEPARTMENT OF MANAGEMENT SCIENCE AND INFORMATION SYSTEMS RUTGERS UNIVERSITY
Recent years have witnessed a rapid adoption of smart mobile devices and their increased pervasiveness into people’s daily life. As a result of this quick development, the demand for better mobile services is increasing with an even faster speed. Recommender systems become essential to deliver the right services to the right mobile users. For instance, Point of Interest (POI) recommendation enables us to recommend the right places to the right users based on their preferences. In this talk, I will discuss several unique challenges for recommendation in mobile business environments, and then introduce how we use advanced data mining techniques to address these challenges. First, many mobile services are location- dependent. I will show how we can effectively model a user’s spatial choice behavior through the example of point of interest recommendation. Along this line, we have proposed a geographical probabilistic factor model framework, which strategically captures user mobility and geographical influence, to model user spatial choice behavior. Extensive experiments demonstrate the effectiveness of the proposed approach. Second, services are usually organized into hierarchy structure such as category hierarchy. I will then introduce a structural user choice model (SUCM) to learn fine-grained user choice patterns by exploiting hierarchy structure. Evaluation on an app adoption data demonstrates that our approach can better capture user choice patterns and thus improve recommendation performance. Finally, privacy becomes a big issue for mobile service adoption. Through an example of app recommendation, I will briefly introduce how recommendation can be improved by considering users’ privacy preferences.
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