TV-Show Retrieval and Classification

Recommender systems are becoming popular tools to aid users in finding interesting and relevant TV-shows and other digital video assets,based on implicitly learned user preferences. In this context, a common assumption is that user preferences can be specified by program types (movie, sports, ...) and that an asset can be labeled by oneor more program types, thus allowing an initial coarse pre-selection of potentially interesting assets. Furthermore each asset has a short textual description, which allows us to investigate whether it might be useful to automatically label assets with program type labels. To that purpose we compared the Vector Space Model with more recent approaches to text classification, such as Logistic Regression and Random Indexing on a large collection of TV-shows descriptions. The experimental results show that LR is the best approach, but RI outperforms VSM under particular conditions.