  Recommender systems are popular tools for information retrieval tasks on a
large variety of web applications and personalized products. In this work, we
propose a Generative Adversarial Network based recommendation framework using a
positive-unlabeled sampling strategy. Specifically, we utilize the generator to
learn the continuous distribution of user-item tuples and design the
discriminator to be a binary classifier that outputs the relevance score
between each user and each item. Meanwhile, positive-unlabeled sampling is
applied in the learning procedure of the discriminator. Theoretical bounds
regarding positive-unlabeled sampling and optimalities of convergence for the
discriminators and the generators are provided. We show the effectiveness and
efficiency of our framework on three publicly accessible data sets with eight
ranking-based evaluation metrics in comparison with thirteen popular baselines.
