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.

本文提出了一种可以有效提高推荐新闻的质量的质量感知型新闻推荐方法 QualityRec，采用基于用户阅读停留时间分布的新闻质量评估方法，并设计内容质量注意力网络将新闻质量信息融入用户兴趣建模，训练辅助的新闻质量预测任务来学习质量感知型的推荐模型，并添加推荐质量正则化的损失来鼓励模型推荐更高质量的新闻，通过在两个真实数据集上进行的实验表明 QualityRec 可以有效提高推荐新闻的整体质量，并减少对低质量新闻的推荐。