  Personalized news recommendation techniques are widely adopted by many online
news feed platforms to target user interests. Learning accurate user interest
models is important for news recommendation. Most existing methods for news
recommendation rely on implicit feedbacks like click behaviors for inferring
user interests and model training. However, click behaviors are implicit
feedbacks and usually contain heavy noise. In addition, they 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 recommendation model training. In our method 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 by jointly training the model
in the click, finish and dwell time prediction tasks to learn an
engagement-aware feed recommendation model. Extensive experiments on real-world
dataset show that our approach can effectively improve the model performance in
terms of both news clicks and user engagement.
