In recent years, journalists have expressed concerns about the increasing trend of news article avoidance, especially within specific domains. This issue has been exacerbated by the rise of recommender systems. Our research indicates that recommender systems should consider avoidance as a fundamental factor. We argue that news articles can be characterized by three principal elements: exposure, relevance, and avoidance, all of which are closely interconnected. To address these challenges, we introduce AWRS, an Avoidance-Aware Recommender System. This framework incorporates avoidance awareness when recommending news, based on the premise that news article avoidance conveys significant information about user preferences. Evaluation results on three news datasets in different languages (English, Norwegian, and Japanese) demonstrate that our method outperforms existing approaches.

新闻领域的推荐系统应考虑新闻文章避免的问题，本研究引入了一种基于避免意识的推荐系统（AWRS），该系统通过考虑用户对新闻文章的避免行为来提供更准确的推荐。评估结果表明，该方法在三个不同语言（英语、挪威语和日语）的新闻数据集上优于现有方法。

透过AWRS探察新闻回避现象：一种具备回避意识的推荐系统