The handling of the enormous offer of TV content is a challenge for every TV user since the number of receivable channels highly increased in recent years. Regular TV guides or newspapers present just a limited number of channel timetables and the user won’t spend additional time to discover the rest of timetables by using other media e.g. the Internet. Hence, the user only focuses on favored channels and interesting content on others won’t be recognized. The result is that the user might not select the most appropriate content regarding his or her interests. Assistive systems and tools are desirable to counteract this problem. We extend a Personal Video Recorder (PVR) with a recommendation system based on a Bayesian classifier and a collaborative approach using social networks like Facebook or Twitter. In case of the Bayesian classifier, the system is analyzing the user’s watching behavior to generate personalized TV program recommendations. With the social network component the user receives recommendations from acquaintances and friends. The recommendations are automatically stored on the system’s internal hard disc drive for the user to watch. This paper presents the current state of development by introducing the system’s architecture and implemented recommendation mechanisms.
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- A User Supporting Personal Video Recorder Based on a Generic Bayesian Classifier and Social Network Recommendations
- Springer Berlin Heidelberg
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