Semi-supervised Learning for Stream Recommender Systems

Recommender systems suffer from an extreme data sparsity that results from a large number of items and only a limited capability of users to perceive them. Only a small fraction of items can be rated by a single user. Consequently, there is plenty of unlabelled information that can be leveraged by semi-supervised methods. We propose the first semi-supervised framework for stream recommender systems that can leverage this information incrementally on a stream of ratings. We design several novel components, such as a sensitivity-based reliability measure, and extend a state-of-the-art matrix factorization algorithm by the capability to extend the dimensions of a matrix incrementally as new users and items occur in a stream. We show that our framework improves the quality of recommendations at nearly all time points in a stream.