RecGAN

Abstract

Recent studies in recommendation systems emphasize the significance of modeling latent features behind temporal evolution of user preference and item state to make relevant suggestions. However, static and dynamic behaviors and trends of users and items, which highly influence the feasibility of recommendations, were not adequately addressed in previous works. In this work, we leverage the temporal and latent feature modelling capabilities of Recurrent Neural Network (RNN) and Generative Adversarial Network (GAN), respectively, to propose a Recurrent Generative Adversarial Network (RecGAN). We use customized Gated Recurrent Unit (GRU) cells to capture latent features of users and items observable from short-term and long-term temporal profiles. The modification also includes collaborative filtering mechanisms to improve the relevance of recommended items. We evaluate RecGAN using two datasets on food and movie recommendation. Results indicate that our model outperforms other baseline models irrespective of user behavior and density of training data.