DAN: Deep Attention Neural Network for News Recommendation

• Published in: Proceedings of the ... AAAI Conference on Artificial Intelligence 2019-07, Vol.33 (1), p.5973-5980
• Main Authors: Zhu, Qiannan, Zhou, Xiaofei, Song, Zeliang, Tan, Jianlong, Guo, Li
• Format: Article
• Language: English
• ISSN: 2159-5399; 2374-3468
• DOI: 10.1609/aaai.v33i01.33015973

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.