GCAN: A Group-Wise Collaborative Adversarial Networks for Item Recommendation

Recommendation System aims to provide personalized recommendation for different users. Recently, Generative Adversarial Networks based recommendation systems have attracted considerable attention. In previous research, GAN has shown potential and flexibility to learn latent features of users’ preferences. However, GANs are hard to train to converge and waste many processes of fulfilling empty data, especially when meeting with the data sparsity problem. In this paper, we propose a new group-wise framework, namely Group-wise Collaborative Adversarial Networks (GCAN) to solve the data sparsity problem and enable GAN to converge faster. We combine GAN with traditional collaborative filtering methods to generate recommendations (CAN), and then propose binary masking and sample shifting to achieve GCAN. Binary masking separates binary user-item interaction and abstracts group-wise relationship from these binary vectors, while sample shifting is designed to avoid incorrect learning process. A noise corruption parameter is then introduced with experiments to show the robustness of GCAN. We compare GCAN with other baseline methods on Yelp and SC dataset, where GCAN achieves the state-of-the-art performances for personalized item recommendation.