NeuRec: On Nonlinear Transformation for Personalized Ranking
This work addresses the limitation of linear assumptions in recommender systems for users and platforms, though it appears incremental as it builds on existing neural network approaches.
The authors tackled the problem of modeling user-item interactions for personalized recommendations by proposing NeuRec, a neural network model that incorporates non-linear transformations with latent factors, and demonstrated its superior performance on four real-world datasets.
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.