Gaussian Process Neural Network Embeddings for Collaborative Filtering

We propose a Gaussian process approach to collaborative filtering based on the random Fourier feature method. The result is a neural network consisting of two non-linear layers in which only the weights of the middle layer need to be learned, along with the embeddings of each user and item. The model is asymptotically equivalent to a Gaussian process with RBF kernel on the space of concatenated user/item embeddings. It has the advantage of computational scalability and doesn't require defining inducing points required by other Gaussian process approximation methods. The compactness of our representation and interpretability our model can make it attractive to a variety of practitioners in applied settings. We illustrate our model on a political voting dataset, followed by more extensive experiments on several MovieLens datasets of different sizes.