Quality-aware News Recommendation
This work addresses the issue of low-quality news recommendations for users and platforms, representing an incremental improvement by incorporating quality metrics into existing recommendation frameworks.
The paper tackles the problem of news recommendation systems prioritizing clicks over content quality, which can lead to recommending uninformative or clickbait articles. The proposed QualityRec method improves the quality of recommended news while maintaining or slightly enhancing recommendation accuracy, as shown in experiments on two real-world datasets.
News recommendation is a core technique used by many online news platforms. Recommending high-quality news to users is important for keeping good user experiences and news platforms' reputations. However, existing news recommendation methods mainly aim to optimize news clicks while ignoring the quality of news they recommended, which may lead to recommending news with uninformative content or even clickbaits. In this paper, we propose a quality-aware news recommendation method named QualityRec that can effectively improve the quality of recommended news. In our approach, we first propose an effective news quality evaluation method based on the distributions of users' reading dwell time on news. Next, we propose to incorporate news quality information into user interest modeling by designing a content-quality attention network to select clicked news based on both news semantics and qualities. We further train the recommendation model with an auxiliary news quality prediction task to learn quality-aware recommendation model, and we add a recommendation quality regularization loss to encourage the model to recommend higher-quality news. Extensive experiments on two real-world datasets show that QualityRec can effectively improve the overall quality of recommended news and reduce the recommendation of low-quality news, with even slightly better recommendation accuracy.