To extract more powerful and robust feature representations from the rating matrix, they proposed a serial autoencoder structure to improve the model recommendation performance.

We propose an unsupervised method for detecting adversarial attacks in inner layers of autoencoder (AE) networks by maximizing a non-parametric measure of anomalous node activations.