DAN: Deep Attention Neural Network for News Recommendation


  • Qiannan Zhu University of Chinese Academy of Sciences
  • Xiaofei Zhou Chinese Academy of Sciences
  • Zeliang Song Chinese Academy of Sciences
  • Jianlong Tan Chinese Academy of Sciences
  • Li Guo Chinese Academy of Sciences




With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail with the dynamic diversity of news and user’s interests, or ignore the importance of sequential information of user’s clicking selection. In this paper, taking full advantages of convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation. Our DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention-based RNN for capturing richer hidden sequential features of user’s clicks, and combines these features for new recommendation. We conduct experiment on real-world news data sets, and the experimental results demonstrate the superiority and effectiveness of our proposed DAN model.




How to Cite

Zhu, Q., Zhou, X., Song, Z., Tan, J., & Guo, L. (2019). DAN: Deep Attention Neural Network for News Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5973-5980. https://doi.org/10.1609/aaai.v33i01.33015973



AAAI Technical Track: Machine Learning