A hybrid learning approach for TV program personalization

The rapid growth of communication technologies and the invention of set-top-box (STB) and personal digital recorder (PDR) have enabled today's television to receive and store tremendous programs. The abundance of TV programs precipitates a need for personalization tools to help people obtain programs that they really want to watch. User preference learning plays an important role in TV program personalization. In this paper, we introduce a hybrid user preference learning approach for TV program personalization. The learning architecture is designed to integrate multiple learning sources for preference learning, which are explicit input/modification, user viewing history, and user real-time feedback. Among those, learning from user viewing history and learning from user real-time feedback are described in detail. The experimental results proved that the hybrid learning approach outperforms the learning method merely adopting user real-time feedback.