DCFGAN: An adversarial deep reinforcement learning framework with improved negative sampling for session-based recommender systems

Abstract

In recent years, with the development of Internet technology, recommender systems have been widely used by virtue of their ability to meet the personalized needs of users. In order to make full use of users’ interactive behaviors, session-based recommender systems have attracted growing research interest. In previous session-based recommender systems, users' historical interactive behavior is utilized to train and update users' preferences, but users' responses to the current recommendation results (immediate feedback) are not effectively exploited to optimize the recommendation strategy. This leads to the decrease of subsequent recommendation accuracy. Aiming at this problem, based on the Recurrent Neural Network (RNN), this paper combines Reinforcement Learning (RL) and Generative Adversarial Networks (GANs). We fully exploit the users’ immediate feedback with RL, and simultaneously take advantage of GAN to satisfy the requirement of training data brought by RL. Furthermore, we optimize the negative sampling method and propose Deep Generative Adversarial Networks-based Collaborative Filtering (DCFGAN). The experimental results show that this algorithm can effectively improve the recommendation accuracy in session-based recommender systems.