是什么驱动了读者？新闻文章推荐中用户界面类型和人气偏差的在线研究

个性化的新闻推荐系统支持读者在在线新闻平台上找到正确的和相关的文章。在本文中，我们讨论了在奥地利流行的在线新闻平台DiePresse上引入的个性化的、基于内容的新闻介绍，重点关注两个具体方面：(i)用户界面类型和(ii)缓解流行偏差。因此，我们从2020年10月开始进行了为期两周的在线研究，其中我们分析了推荐对两个用户组的影响，即匿名用户和订阅用户，以及三种用户界面类型，即在桌面设备、移动设备和平板设备上。对于用户界面类型，我们发现桌面设备看到推荐的概率最高，而移动设备与推荐交互的概率最高。在缓解受欢迎程度偏见方面，我们发现，个性化的、基于内容的新闻推荐可以导致新闻文章读者受欢迎程度的更平衡的分布。除此之外，我们发现重大事件(如奥地利宣布covid-19封锁和维也纳恐怖袭击)都影响了匿名用户和订阅用户的流行文章的一般消费行为。

原文题目：What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations

原文：Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalized, content-based news recommendations on DiePresse, a popular Austrian online news platform, focusing on two specific aspects: (i) user interface type, and (ii) popularity bias mitigation. Therefore, we conducted a two-weeks online study that started in October 2020, in which we analyzed the impact of recommendations on two user groups, i.e., anonymous and subscribed users, and three user interface types, i.e., on a desktop, mobile and tablet device. With respect to user interface types, we find that the probability of a recommendation to be seen is the highest for desktop devices, while the probability of interacting with recommendations is the highest for mobile devices. With respect to popularity bias mitigation, we find that personalized, content-based news recommendations can lead to a more balanced distribution of news articles' readership popularity in the case of anonymous users. Apart from that, we find that significant events (e.g., the COVID-19 lockdown announcement in Austria and the Vienna terror attack) influence the general consumption behavior of popular articles for both, anonymous and subscribed users.