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Recently, deep learning has become a preferred choice for performing tasks in diverse application domains such as computer vision, natural language processing, sensor data analytics for healthcare, and collaborative filtering for personalized item recommendation. In addition, the  Generative Adversarial Networks  (GAN) has become one of the most popular frameworks for training machine learning models. Motivated by the huge success of GAN and deep learning on a wide range of fields, this paper explores an effective way to exploit both techniques into the  collaborative filtering  task for the accurate recommendation. We have noticed that the IRGAN and GraphGAN are pioneering methods that successfully apply GAN to recommender systems. However, we point out an issue regarding the employment of standard  matrix factorization  (MF) as their basic model, which is linear and unable to capture the non-linear, subtle latent factors underlying user-item interactions. Our proposed recommendation framework, named  Collaborative Adversarial Autoencoders  (CAAE), significantly extends the conventional IRGAN and GraphGAN as summarized below: 1) we use Autoencoder, which is one of the most successful deep neural networks, as our generator, instead of using the MF model; 2) we employ  Bayesian personalized ranking  (BPR) as our discriminative model; and 3) we incorporate another generator model into our framework that focuses on generating  negative items , which are items that a given user may  not  be interested in. We empirically test our framework using three real-life datasets along with four evaluation metrics. Owing to those extensions, our proposed framework not only produces considerably higher recommendation accuracy than the conventional GAN-based recommenders (i.e., IRGAN and GraphGAN), but also outperforms the other state-of-the-art top-N recommenders (i.e., BPR, PureSVD, and FISM).

Collaborative Adversarial Autoencoders: An Effective Collaborative Filtering Model Under the GAN Framework