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Keyword-Based TV Program Recommendation

Notwithstanding the success of collaborative filtering algorithms for item recommendation there are still situations in which there is a need for content-based recommendation, especially in new-item scenarios, e.g. in streaming broadcasting. Since video content is hard to analyze we use documents describing the videos to compute item similarities. We do not use the descriptions directly, but us...

JaWa: Java with Assertions

TV Scout: Guiding Users from Printed TV Program Guides to Personalized TV Recommendation

In this paper, we present TV Scout, a recommendation system providing users with personalized TV schedules. The TV Scout architecture addresses the “cold-start” problem of information filtering systems, i.e. that filtering systems have to gather information about the user’s interests before they can compute personalized recommendations. Traditionally, gathering this information involves upfront...

Automatic Program Extraction From TV Streams

This demo presents a method of detecting television programs in TV streams. The objective is to automatically detect the precise start and end of broadcasted television programs. The method first detects inter-programs (commercials, trailers. . . ) as repeated sequences in the broadcast stream, and then deduces the programs boundaries. The extracted programs can then be stored in a database to ...

Personalized Electronic Program Guides for Digital TV

access to greater and greater amounts of electronic information, we are faced with significant problems when it comes to finding the right information at the right time—the essence of the information-overload problem. One of the proposed solutions to this problem is to develop technologies for automatically learning about the implicit and explicit preferences of individual users to customize an...