Neural Tensor Model for Learning Multi-Aspect Factors in Recommender Systems

Abstract

Recommender systems often involve multi-aspect factors. For example, when shopping for shoes online, consumers usually look through their images, ratings, and product's reviews before making their decisions. To learn multi-aspect factors, many context-aware models have been developed based on tensor factorizations. However, existing models assume multilinear structures in the tensor data, thus failing to capture nonlinear feature interactions. To fill this gap, we propose a novel nonlinear tensor machine, which combines deep neural networks and tensor algebra to capture nonlinear interactions among multi-aspect factors. We further consider adversarial learning to assist the training of our model. Extensive experiments demonstrate the effectiveness of the proposed model.