A Collaborative Filtering Framework Based on Variational Autoencoders and Generative Adversarial Networks

The generation models such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) have been demonstrated to be of high effectiveness in standard collaborative filtering applications. However, the conventional VAE can't capture the data distribution well when the data is sparse or the auxiliary information is added, resulting in low recommendation accuracy. In this paper, we propose a novel VAE-GAN-based collaborative filtering (CF) framework, named CF-VAE-GAN, to provide higher accuracy in recommendation. First, auxiliary information such as user comments and item multimedia features are added to VAE. And then, we use the discriminator of GAN to improve the reconstruction objective of VAE. Finally, we design two CF-VAE-GAN models of users and items, respectively. Empirical results indicate that our method outperforms state-of-the-art methods in terms of Recall and NDCG.