A TV News Recommendation System with Automatic Recomposition
In this paper, we propose a new recommendation system for a TV news with automatic recomposition. For the time consuming browsing of the TV news articles, we propose three modes of presentation, the digest mode, the relaxed mode, and the normal mode, where each presentation length is different. To make these presentation, TV news articles are decomposed, analyzed, and stored in the database scene by scene. Then, the system selects desired items and synthesizes these scenes into a presentation based on a user’s profile. For the profile of the user, we use a keyword vector and a category vector of news articles. The system is designed so that user’s control to the system becomes minimum. Therefore, a user only plays, skips, plays previous, and rewinds news articles in the system as same as an ordinary TV. However, different from an ordinary TV, the system collects user’s behavior while he uses the system. Based on this information, the system updates the user’s profile. We also show preliminary experimental results.
KeywordsRecommendation System News Article Video Segment News Item News Program
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