Deep Representation Learning using Multilayer Perceptron and Stacked Autoencoder for Recommendation Systems

Deep learning-based collaborative filtering methods are studied in recommendation systems as efficient feature mapping techniques. The aim of these methods is to project the users and items to a common representation space and obtain their latent features. Although these methods have been widely used in the literature, they suffer from the limited expressiveness of Dot product function. In other words, Dot product cannot describe different impacts of various latent factors. To solve this issue, we propose a novel recommender system named Deep-MSR which exploits the multilayer perceptron (MLP) neural network and stacked auto-encoder network (SAN) to extract item latent factors and user latent factors from user-item interaction matrix. The obtained latent factors are used in the proposed rating prediction module which integrates user preferences and item features in the recommendation process. Our experiments on two well-known datasets show that our method can outperform the competitive baseline recommendation methods.