IGAN: A collaborative filtering model based on Improved Generative Adversarial Networks for recommendation

Abstract

In recent years, Generative Adversarial Networks (GANs) have been successfully applied to collaborative filtering for implicit feedback. However, the ability of GAN to learn user interest distributions is greatly reduced due to its difficulty in characterizing the features. In this paper, a collaborative filtering model based on Improved Generative Adversarial Networks (IGAN) is proposed to address the above issue. In IGAN, we first introduce the independent encoder and generator to learn features representation during adversarial training. Then, the Kullback–Leibler (KL) loss and reconstruction loss are added as the penalty term to further fit the interest distribution of users and achieve higher recommendation accuracy. Finally, this paper conducts extensive experiments on three real public datasets, showing that the IGAN method has a maximum improvement of 5.26% and 5.43% in P@5 and NDCG@5 compared with the current GAN-based methods.