Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems

Online recommender systems are an important tool that people use to find new music. To generate recommendations, many systems rely on tag representations of music. Such systems, however, suffer from tag sparsity, whereby tracks lack a strong tag representation. Current state-of-the-art techniques that reduce this sparsity problem create hybrid systems using multiple representations, for example both content and tags. In this paper we present a novel hybrid representation that augments sparse tag representations without introducing content directly. Our hybrid representation integrates pseudo-tags learned from content into the tag representation of a track, and a dynamic weighting scheme limits the number of pseudo-tags that are allowed to contribute. Experiments demonstrate that this method allows tags to remain dominant when they provide a strong representation, and pseudo-tags to take over when tags are sparse. We show that our approach significantly improves recommendation quality not only for queries with a sparse tag representation but also those that are well-tagged. Our hybrid approach has potential to be extended to other music representations that are used for recommendation but suffer from data sparsity, such as user profiles.