PredicTV: A Behavior-Oriented Real Time Recommender for TV Programs

Wenjing Fang; Zhiyuan Cai; Xiaodong Wang; Kenny Q. Zhu

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PredicTV: A Behavior-Oriented Real Time Recommender for TV Programs

Wenjing Fang, Zhiyuan Cai, Xiaodong Wang, Kenny Q. Zhu

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240

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Funding: none

Opened online:28 December 2015

Accepted by: none

Citation: Wenjing Fang, Zhiyuan Cai, Xiaodong Wang.PredicTV: A Behavior-Oriented Real Time Recommender for TV Programs[OL]. [28 December 2015] http://en.paper.edu.cn/en_releasepaper/content/4666173

With112channelsandabout30,000programstowatcheveryweek,televisionviewersin China are overwhelmed with choices. Finding out what to watch can be a time-consuming and frustrating process. In this paper, we present a system that leverages individuals’ viewing behaviors and the useful information about the TV programs to make real-time, dynamic program recommendations to the TV viewers. The system builds a vector-space based preference model for each user by combining the viewing patterns and the contents of the program that viewed by the users. Recommendation of future programs is done by selecting the best set of programs that matches the user’s viewing model.

Keywords:TV program recommender, information extraction, vector space model

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