DeepVT: Deep View-Temporal Interaction Network for News Recommendation

Abstract

Personalized news recommendation aims to provide people with customized content, which can effectively improve the reading experience. Because user interests in news are diverse and changeable, how to learn accurate user representations is the core challenge in news recommendation. However, most of the previous works only apply news-level representation for user modeling directly, the views of news, such as title, abstract, and category, are only implied and compressed into a single vector of news, which makes it impossible for different views in different news to interact with each other. In this paper, we first focus on the view-level information for user modeling and propose Deep View-Temporal Interaction Network (DeepVT) for news recommendation. It mainly contains two components, i.e., 2D semi-causal convolutional neural network (SC-CNN) and multi-operator attention (MoA). SC-CNN can synthesize interaction information at the view-level and temporal information at the news-level simultaneously and efficiently. And MoA integrates different similarity operators in self-attention functions to avoid attention bias and enhance robustness. By collaboration with SC-CNN, the global interaction at the view-level becomes more sufficient. Experiments on a large-scale real-world dataset, Microsoft News Dataset (MIND), show that our model outperforms previous models in terms of all metrics significantly.