NeuRec: On nonlinear transformation for personalized ranking

Publication Type:
Conference Proceeding
IJCAI International Joint Conference on Artificial Intelligence, 2018, 2018-July pp. 3669 - 3675
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© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. 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: userbased 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.
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