This article explores the use of deep neural networks for learning the interaction function from data. We propose a hybrid recommendation approach that combines collaborative filtering (CF) and content-based filtering (CBF) in an architecture based on both models : generalized matrix factorization and multilayer perceptron. Extensive experiments on the MovieLens-1M database show significant improvements in our approach compared to existing methods, especially for the cold start situation.