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.
@article{arxiv.1805.03002,
title = {NeuRec: On Nonlinear Transformation for Personalized Ranking},
author = {Shuai Zhang and Lina Yao and Aixin Sun and Sen Wang and Guodong Long and Manqing Dong},
journal= {arXiv preprint arXiv:1805.03002},
year = {2018}
}