An efficient method for autoencoder-based collaborative filtering

Collaborative filtering (CF) is a widely used technique in recommender systems. With rapid development in deep learning, neural network-based CF models have gained great attention in the recent years, especially autoencoder-based CF model. Although autoencoder-based CF model is faster compared with some existing neural network-based models (eg, Deep Restricted Boltzmann Machine-based CF), it is still impractical to handle extremely large-scale data. In this paper, we practically verify that most non-zero entries of the input matrix are concentrated in a few rows. Considering this sparse characteristic, we propose a new method for training autoencoder-based CF. We run experiments on two popular datasets MovieLens 1 M and MovieLens 10 M. Experimental results show that our algorithm leads to orders of magnitude speed-up for training (stacked) autoencoder-based CF model while achieving comparable performance compared with existing state-of-the-art models.