Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced by the NRS and identify the possible solutions from the state-of-the-art. Our discussion is divided into two parts. In the first part, we present an overview of the recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in the NRS. We also talk about two popular classes of models that have been successfully used in recent years. In the second part, we focus on the deep neural networks as solutions to build the NRS. Different from previous surveys, we study the effects of news recommendations on user behaviors and try to suggest possible remedies to mitigate those effects. By providing the state-of-the-art knowledge, this survey can help researchers and professional practitioners have a better understanding of the recent developments in news recommendation algorithms. In addition, this survey sheds light on the potential new directions.

如今，越来越多的新闻读者在线阅读新闻，他们可以访问来自多个来源的数百万篇新闻文章。为了帮助用户找到正确和相关的内容，新闻推荐系统 (NRS) 被开发来缓解信息过载问题并建议新闻读者可能感兴趣的新闻项目。在本文中，我们强调了 NRS 面临的主要挑战，并从最先进的技术中确定了可能的解决方案。我们的讨论分为两部分。在第一部分中，我们概述了 NRS 中使用的推荐解决方案、数据集、超出准确性的评估标准和推荐平台。我们还讨论了近年来成功使用的两类流行模型。在第二部分，我们专注于将深度神经网络作为构建 NRS 的解决方案。与之前的调查不同，我们研究了新闻推荐对用户行为的影响，并试图提出可能的补救措施来减轻这些影响。通过提供最先进的知识，这项调查可以帮助研究人员和专业从业者更好地了解新闻推荐算法的最新发展。此外，这项调查揭示了潜在的新方向。这项调查可以帮助研究人员和专业从业者更好地了解新闻推荐算法的最新发展。此外，这项调查揭示了潜在的新方向。这项调查可以帮助研究人员和专业从业者更好地了解新闻推荐算法的最新发展。此外，这项调查揭示了潜在的新方向。