A Recommender System Based on Deep Neural Network and Matrix Factorization for Collaborative Filtering

In this paper, a revised recommendation system is constructed that ensembles deep neural network and matrix factorization under its framework and uses the explicit feedback for collaborative filtering. Recent works used deep neural network in recommendation for processing auxiliary attributes, but their interaction function is just an inner product on latent features of users and items. For modelling the recommendation system in this research, multi-layer perceptron was used to learn the interaction function. Experiments show significant decrease in MAE and RMSE to be 0.69 and 0.94 respectively, which is comparatively better than general collaborative filtering methods.