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News recommendation is critical for personalized news access. Existing newsrecommendation methods usually infer users' personal interest based on theirhistorical clicked news, and train the news recommendation models by predictingfuture news clicks. A core assumption behind these methods is that news clickbehaviors can indicate user interest. However, in practical scenarios, beyondthe relevance between user interest and news content, the news click behaviorsmay also be affected by other factors, such as the bias of news presentation inthe online platform. For example, news with higher positions and larger sizesare usually more likely to be clicked. The bias of clicked news may bringnoises to user interest modeling and model training, which may hurt theperformance of the news recommendation model.  In this paper, we propose a bias-aware personalized news recommendationmethod named DebiasRec, which can handle the bias information for more accurateuser interest inference and model training. The core of our method includes abias representation module, a bias-aware user modeling module, and a bias-awareclick prediction module. The bias representation module is used to modeldifferent kinds of news bias and their interactions to capture their jointeffect on click behaviors. The bias-aware user modeling module aims to inferusers' debiased interest from the clicked news articles by using their biasinformation to calibrate the interest model. The bias-aware click predictionmodule is used to train a debiased news recommendation model from the biasedclick behaviors, where the click score is decomposed into a preference scoreindicating user's interest in the news content and a news bias score inferredfrom its different bias features. Experiments on two real-world datasets showthat our method can effectively improve the performance of news recommendation.

DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation