Transformer-based DKN for News Recommendation

In recent years, deep learning has been widely used in news recommendation systems. In the previous personalized news recommendation, a large number of CF-based methods, content-based or hybrid methods have been proposed. But most of the works are only modeling the user's interaction history, ignoring the hidden meaning of the user's continuous behaviors. In this paper, we propose to adopt the powerful Transformer model in order to understand the hidden meaning of the user's continuous behaviors in news recommendations. The experimental results prove the superiority of the transformer, and the AUC has been significantly improved as compared to the original model.