论文标题
新闻推荐系统：对最新进度，挑战和机遇的评论
News Recommender System: A review of recent progress, challenges, and opportunities
论文作者
论文摘要
如今，越来越多的新闻读者倾向于在线阅读新闻，他们可以从多个来源访问数百万新闻文章。为了帮助用户找到正确和相关的内容，开发了新闻推荐系统（NRS），以减轻信息超载问题并建议用户可能感兴趣的新闻项目。在本文中，我们强调了新闻推荐域面临的主要挑战，并确定了最先进的解决方案。由于使用深度学习模型的建筑推荐系统的快速增长，我们将讨论分为两个部分。在第一部分中，我们概述了NRS中使用的常规推荐解决方案，数据集，评估标准和建议平台。在第二部分中，我们解释了NRS中应用的基于深度学习的建议解决方案。与以前的调查不同，我们还研究了新闻建议对用户行为的影响，并尝试提出减轻这些影响的可能补救措施。通过提供最先进的知识，这项调查可以帮助研究人员和实践专业人员了解新闻推荐算法中的发展。它还阐明了潜在的新方向
Nowadays, more and more news readers tend to read news online where they have access to millions of news articles from multiple sources. In order to help users to find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that users might be interested in. In this paper, we highlight the major challenges faced by the news recommendation domain and identify the possible solutions from the state-of-the-art. Due to the rapid growth of building recommender systems using deep learning models, we divide our discussion in two parts. In the first part, we present an overview of the conventional recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in NRS. In the second part, we explain the deep learning-based recommendation solutions applied in NRS. Different from previous surveys, we also study the effects of news recommendations on user behavior and try to suggest the possible remedies to mitigate these effects. By providing the state-of-the-art knowledge, this survey can help researchers and practical professionals in their understanding of developments in news recommendation algorithms. It also sheds light on potential new directions
