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 use their keywords as an intermediate level of representation. We argue that a nearest-neighbor approach relying on unrestricted keywords deserves a special definition of similarity that also takes word similarities into account. We define such a similarity measure as a divergence measure of smoothed keyword distributions. The smoothing is done on the basis of co-occurrence probabilities of the present keywords. Thus co-occurrence similarity of words is also taken into account. We have evaluated keyboard-based recommendations with a dataset collected by the BBC and on a subset of the MovieLens dataset augmented with plot descriptions from IMDB. Our main conclusions are (1) that keyword-based rating predictions can be very effective for some types of items, and (2) that rating predictions are significantly better if we do not only take into account the overlap of keywords between two documents, but also the mutual similarities between keywords.