NeuRec: On Nonlinear Transformation for Personalized Ranking

NeuRec: On Nonlinear Transformation for Personalized Ranking

Shuai Zhang, Lina Yao, Aixin Sun, Sen Wang, Guodong Long, Manqing Dong

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3669-3675. https://doi.org/10.24963/ijcai.2018/510

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 establish 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 focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.
Keywords:
Machine Learning: Neural Networks
Machine Learning: Deep Learning
Machine Learning: Recommender Systems