Preference Networks: Probabilistic Models for Recommendation Systems
This work addresses the need for more effective and integrated recommendation systems for users dealing with large datasets, though it appears incremental as it builds on existing filtering methods.
The authors tackled the problem of recommendation systems by proposing Preference Networks (PN), a unified probabilistic framework that combines content-based and collaborative filtering into a conditional Markov random field, achieving improved performance on movie rating data.
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-$N$ recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.