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arXiv · 1805.03002

NeuRec: On Nonlinear Transformation for Personalized Ranking

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

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.

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Shuai Zhang, Lina Yao, Aixin Sun, Sen Wang, Guodong Long, Manqing Dong. 2018-05-08. NeuRec: On Nonlinear Transformation for Personalized Ranking. https://arxiv.org/abs/1805.03002

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