Fastformer: Additive Attention Can Be All You Need

News recommendation is critical for personalized news access. Existing news recommendation methods usually infer users’ personal interest based on their historical clicked news, and train the news recommendation models by predicting future news clicks. A core assumption behind these methods is that news click behaviors can indicate user interest. However, in practical scenarios, beyond the relevance between user interest and news content, the news click behaviors may also be affected by other factors, such as the bias of news presentation in the online platform. For example, news with higher positions and larger sizes are usually more likely to be clicked. Experiments on two real-world datasets show that our method can effectively improve the performance of news recommendation. 2021: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang Ranked #1 on News Recommendation on MIND https://arxiv.org/pdf/2108.09084v6.pdf