Rating prediction is a key task of e-commerce recommendation mechanisms. Recent studies in social recommendation enhance the performance of rating predictors by taking advantage of user relationship. However, these prediction approaches mostly rely on user personal information which is a privacy threat. In this paper, we present a simple rating prediction approach relying on deep neural networks that does not reveal any personal information. Experiments on real-world data sets showed that the approach outperforms state-of-the-art in terms of RMSE and MAE scores for both warm-start and cold-start problems.