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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. The bias of clicked news may bring noises to user interest modeling and model training, which may hurt the performance of the news recommendation model. In this paper, we propose a bias-aware personalized news recommendation method named DebiasRec, which can handle the bias information for more accurate user interest inference and model training. The core of our method includes a bias representation module, a bias-aware user modeling module, and a bias-aware click prediction module. The bias representation module is used to model different kinds of news bias and their interactions to capture their joint effect on click behaviors. The bias-aware user modeling module aims to infer users debiased interest from the clicked news articles by using their bias information to calibrate the interest model. The bias-aware click prediction module is used to train a debiased news recommendation model from the biased click behaviors, where the click score is decomposed into a preference score indicating users interest in the news content and a news bias score inferred from its different bias features. Experiments on two real-world datasets show that our method can effectively improve the performance of news recommendation.
User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest. However, user interes
Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. However, these methods usually hav
The most important task in personalized news recommendation is accurate matching between candidate news and user interest. Most of existing news recommendation methods model candidate news from its textual content and user interest from their clicked
Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively studied over decades and has achieved notable success in improving users
User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have be