## 2 Problem Definition and Motivation: Modeling a News Recommending System  
The collapse of the traditional advertising model [17], the web's free news culture [18] and the growing role of the platforms in news distribution [19; 20] have made it increasingly difficult for news organizations to cope with editorial and commercial standards [21]. Initially, publishers saw the platforms as an ally to help them boost content visibility and brand awareness; soon, they realized that this evolving publisher-platform partnership is unequal; platforms wield more power over user data and earn significantly more advertising revenue than publishers [22; 23]. Amid increasing demands to two and retain users' attention, the use of algorithmic and data-driven techniques is gaining more and more ground in the media industry; they are used to automate workflows by modeling human-centered practices, thereby assigning new roles to both machines and media professionals [24; 25]. News recommendations bear substantial benefits for all parties involved: first, they comprise an effective content monetization tool in terms of building traffic, engagement and loyalty [26]. Second, they help readers discover the depth of the outlets' reporting; The _New York Times_, for example, publishes approximately 250 stories per day. Algorithmic curation is used to propose content facilitating users to encounter news stories that might prove helpful and interesting to them and might otherwise not find while motivating the medium to keep producing a wide range of content [27], as the tailored delivery of news allows republishing content on a much broader scale. Finally, NRS have shown to be a useful tool for helping users deal with information overload [28]. Users are bombarded with news and information from different news outlets, social network posts, notifications, emails, etc., a situation which affects their attention span while making it increasingly difficult for them to find content of interest, at the right moment, in the right form [29]. Despite the acknowledged merits of news personalization, the technical challenge for offering effective recommendations is high [9; 30] and particularly expensive [31]. [PERSON] and his colleagues [32] provide a comprehensive review of the many challenges associated with algorithmic accuracy and user profiling in news recommender systems. However, apart from accuracy and user profiling which comprise typical algorithmic challenges, news recommendation systems present a special case considering their civic function in the direction of an informed citizenry [33]. From a normative perspective, news provides the necessary information for citizens to think, discuss and decide wisely, to participate in political life and thus democracy to function [34]. For that reason, much work on news recommendations focuses on exposure diversity as a design principle for news recommender systems (see [35; 36; 37]). The idea is that for a functioning democracy, users should encounter a variety of themes, opinions and ideas. [PERSON] and her colleagues [38] argue that \"recommendation systems can be instrumental in realizing or obstructing public values and freedom of expression in a digital society; much depends on the design of these systems\" (p. 3). Recent diversity preoccupations echo older concerns arguing that the retreat from human editorial decision making in favor of machine choices might prove problematic. [PERSON]'s [39] widely known hypothesis over filter bubbles refers to algorithmic filtering that tends to promote tailored content based on users' pre-existing attitudes, interests and prejudices, leading citizens into content 'bubbles'. In other words, personalized news offerings may result in people losing the common ground of news [40] by producing \"different individual realities\" [41] that hinder debate and amplify audience fragmentation and polarization [42]. Relevant work so far provides mixed results.