  Modeling user-item interaction patterns is an important task for personalized
recommendations. Many recommender systems are based on the assumption that
there exists a linear relationship between users and items while neglecting the
intricacy and non-linearity of real-life historical interactions. In this
paper, we propose a neural network based recommendation model (NeuRec) that
untangles the complexity of user-item interactions and establishes an
integrated network to combine non-linear transformation with latent factors. We
further design two variants of NeuRec: user-based NeuRec and item-based NeuRec,
by concentrating on different aspects of the interaction matrix. Extensive
experiments on four real-world datasets demonstrated their superior
performances on personalized ranking task.
