A Sparse Probabilistic Model of User Preference Data

Abstract

Modern recommender systems rely on user preference data to understand, analyze and provide items of interest to users. However, for some domains, collecting and sharing such data can be problematic: it may be expensive to gather data from several users, or it may be undesirable to share real user data for privacy reasons. We therefore propose a new model for generating realistic preference data. Our Sparse Probabilistic User Preference (SPUP) model produces synthetic data by sparsifying an initially dense user preference matrix generated by a standard matrix factorization model. The model incorporates aggregate statistics of the original data, such as user activity level and item popularity, as well as their interaction, to produce realistic data. We show empirically that our model can reproduce real-world datasets from different domains to a high degree of fidelity according to several measures. Our model can be used by both researchers and practitioners to generate new datasets or to extend existing ones, enabling the sound testing of new models and providing an improved form of bootstrapping in cases where limited data is available.