High-order autoencoder with data augmentation for collaborative filtering

Abstract

Early DNN-based collaborative filtering (CF) approaches have demonstrated their superior performance than traditional CF such as Matrix Factorization. However, such approaches treat each user–item interaction as separate data and thus overlook the intrinsic relationships among data instances. Inspired by the discovery that the autoencoder architecture can force the hidden representation to capture information about the structure of the graph data, in this work, we propose a novel framework called High-order Autoencoder based Collaborative Filtering (HACF) that enhances the classic NeuMF framework with autoencoders for capturing latent high-order connectivity signals in the user–item interaction graph. Specifically, each user–item pair is augmented with higher-order neighbours and input to two sets of autoencoders, one set for the users and the other for the items. All the autoencoders in one set share parameters so increasing the number of autoencoders does not increase the model size.We have conducted extensive experiments on four popular public benchmark datasets with different sparsity. The overall comparison results demonstrate the advantages of autoencoder-based methods and show that our framework outperforms some state-of-the-art DNN-based collaborative filtering approaches.