  Item recommendation is a personalized ranking task. To this end, many
recommender systems optimize models with pairwise ranking objectives, such as
the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) ---
the most widely used model in recommendation --- as a demonstration, we show
that optimizing it with BPR leads to a recommender model that is not robust. In
particular, we find that the resultant model is highly vulnerable to
adversarial perturbations on its model parameters, which implies the possibly
large error in generalization.
  To enhance the robustness of a recommender model and thus improve its
generalization performance, we propose a new optimization framework, namely
Adversarial Personalized Ranking (APR). In short, our APR enhances the pairwise
ranking method BPR by performing adversarial training. It can be interpreted as
playing a minimax game, where the minimization of the BPR objective function
meanwhile defends an adversary, which adds adversarial perturbations on model
parameters to maximize the BPR objective function. To illustrate how it works,
we implement APR on MF by adding adversarial perturbations on the embedding
vectors of users and items. Extensive experiments on three public real-world
datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it
outperforms BPR with a relative improvement of 11.2% on average and achieves
state-of-the-art performance for item recommendation. Our implementation is
available at: https://github.com/hexiangnan/adversarial_personalized_ranking.
