Selecting keywords for content based recommendation

Abstract

The continued growth of online content makes personalized recommendation an increasingly important tool for media consumption. While collaborative filtering techniques have shown to be very successful in stable collections, content based approaches are necessary for recommending new items. Content based recommendation uses the similarity between new items and consumed items to predict whether a new item is interesting for the user. The similarity is computed by comparing the content or the meta-data of the items. In this paper we consider recommendation of TV-broadcasts for which meta-data and synopses are available. We thereby concentrate on the new item problem. We investigate the value of different types of meta-data provided by the broadcaster or extracted from synopsis. We show that extracted keywords are better suited for recommendation than manually assigned keywords. Furthermore we show that the number of keywords used is of great importance. Using a rather small number of keywords to present an item yields the best results for recommendation.