NeuRec: On Nonlinear Transformation for Personalized Ranking

International Joint Conferences on Artificial Intelligence Organization 2018-01-01

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.

4605 Data Management and Data Science; 46 Information and Computing Sciences; 4609 Information Systems; 4611 Machine Learning; anzsrc-for: 4605 Data Management and Data Science; anzsrc-for: 46 Information and Computing Sciences; anzsrc-for: 4609 Information Systems; anzsrc-for: 4611 Machine Learning; conference paper; http://purl.org/coar/resource_type/c_5794

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