Large-scale Ordinal Collaborative Filtering

This paper proposes a hierarchical probabilistic model for ordinal matrix factorization by actively modelling the ordinal nature of ranking data, which is typical of large-scale collaborative filtering tasks. Two algorithms are presented for inference, one based on Gibbs sampling and one based on variational Bayes. The model is evaluated on a collaborative filtering task, where users have rated a collection of movies and the system is asked to predict their ratings for other movies. The Netflix data set is used for evaluation, which consists of around 100 million ratings. Using root mean-squared error (RMSE) as an evaluation metric, results show that the suggested model improves similar factorization techniques. Results also show how Gibbs sampling outperforms variational Bayes on this task, despite the large number of ratings and model parameters.