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.

翻译：近年来，新闻回避现象日益加剧，尤其在特定领域内，这一趋势引发了新闻从业者的普遍担忧。推荐系统的兴起进一步恶化了该问题。我们的研究表明，回避应被视为推荐系统的一项基本考量因素。我们认为，新闻文章可通过三个相互关联的核心要素进行表征：曝光度、相关性与回避倾向。为应对这些挑战，我们提出了AWRS——一种回避感知的推荐系统。该框架基于“新闻回避行为传递着用户偏好的关键信息”这一前提，在推荐新闻时融入回避感知机制。在三种不同语言（英语、挪威语和日语）新闻数据集上的评估结果表明，我们的方法优于现有方案。