  Item recommendation is the task of predicting a personalized ranking on a set
of items (e.g. websites, movies, products). In this paper, we investigate the
most common scenario with implicit feedback (e.g. clicks, purchases). There are
many methods for item recommendation from implicit feedback like matrix
factorization (MF) or adaptive knearest-neighbor (kNN). Even though these
methods are designed for the item prediction task of personalized ranking, none
of them is directly optimized for ranking. In this paper we present a generic
optimization criterion BPR-Opt for personalized ranking that is the maximum
posterior estimator derived from a Bayesian analysis of the problem. We also
provide a generic learning algorithm for optimizing models with respect to
BPR-Opt. The learning method is based on stochastic gradient descent with
bootstrap sampling. We show how to apply our method to two state-of-the-art
recommender models: matrix factorization and adaptive kNN. Our experiments
indicate that for the task of personalized ranking our optimization method
outperforms the standard learning techniques for MF and kNN. The results show
the importance of optimizing models for the right criterion.
