The performance of collaborative filtering recommender systems can suffer when data is sparse, for example in distributed situations. In addition popular algorithms such as memory-based collaborative filtering are rather ad-hoc, making principled improvements difficult. In this paper we focus on a simple recommender based on naïve Bayesian techniques, and explore two different methods of modelling probabilities. We find that a Gaussian model for rating behaviour works well, and with the addition of a Gaussian-Gamma prior it maintains good performance even when data is sparse.
Reference REF shows two different methods of modeling probabilities: a Gaussian model for rating behavior, with the addition of a Gaussian-Gamma prior, and Multimodal model, which is equivalent to taking maximum likelihood estimates of the parameters of a multinomial distribution.
Experiments in Bayesian Recommendation
AWIC
Computer Science