FeedRec: News Feed Recommendation with Various User Feedbacks
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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.
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