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Automatic construction of personalized TV news programs

In this paper, we study the automatic construction of personalized TV News programs, where we want to build a program with predefined duration and maximum content value for a specific user. We combine video indexing techniques to parse TV News recordings into stories, and information filtering techniques to select stories which are most adequate given the user profile. We formalize the selection process as an optimization problem, and we study how to take into account duration in the selection of stories. Experiments show that a simple heuristic can provide high quality selection with little computation. We also describe two prototypes, which implement two different mechanisms for the construction of user profiles:explicit specification, using a category-based model,
implicit specification, using a keyword-based model.
