TV-Show Retrieval and Classification

Recommender systems are popular tools to aid users in finding interesting and relevant TV shows and other digital video assets, based on implicitly defined user preferences. In this context, a common assumption is that user preferences can be specified by program types (such as documentary, sports), and that an asset can be labeled by one or more program types, thus allowing an initial coarse preselection of potentially interesting assets. Furthermore each asset has a short textual description, which allows us to investigate whether it is possible to automatically label assets with program type labels. We compare the Vector Space Model (vsm) with more recent approaches to text classification, such as Logistic Regression (lr) and Random Indexing (ri) on a large collection of TV-show descriptions. The experimental results show that lr is the best approach, but ri outperforms vsm under particular conditions.