  Accurate user interest modeling is important for news recommendation. Most
existing methods for news recommendation rely on implicit feedbacks like click
for inferring user interests and model training. However, click behaviors
usually contain heavy noise, and cannot help infer complicated user interest
such as dislike. Besides, the feed recommendation models trained solely on
click behaviors cannot optimize other objectives such as user engagement. In
this paper, we present a news feed recommendation method that can exploit
various kinds of user feedbacks to enhance both user interest modeling and
model training. We propose a unified user modeling framework to incorporate
various explicit and implicit user feedbacks to infer both positive and
negative user interests. In addition, we propose a strong-to-weak attention
network that uses the representations of stronger feedbacks to distill positive
and negative user interests from implicit weak feedbacks for accurate user
interest modeling. Besides, we propose a multi-feedback model training
framework to learn an engagement-aware feed recommendation model. Extensive
experiments on a real-world dataset show that our approach can effectively
improve the model performance in terms of both news clicks and user engagement.
