## 3 Design Challenges for News Recommenders  
An algorithm can be defined as a series of steps undertaken to solve a particular problem or accomplish a defined outcome [48]. In the context of news recommenders, the task of algorithms is to structure and order the pool of available news according to predetermined principles [49]. Algorithms though are embedded with values, biases or ideologies [50] that can influence an effective and unbiased provision of information [40]. [PERSON] [48] speaks of algorithmic power premised in algorithms' atomic decisions, including prioritization, classification, association and filtering. Some algorithmic power may be exerted intentionally, while other aspects might be unintended side effects rooted in design decisions, objective descriptions, constraints and business rules embedded in the system, major changes that have happened over time, as well as implementation details that might be relevant (p. 404). News recommender systems are often classified into four main categories:  
1. _Collaborative filtering_: Items are recommended to a user based upon values assigned by other people with similar taste. Users are grouped into clusters on the basis of their preferences, habits or content ranking [51]; in practice, collaborative filtering automates the process of 'word-of-mouth' recommendations [52] and is found to be the most common approach in the recommender system literature [32]. 2. _Popularity filtering_: In this case, items are rated for their general popularity among all users; it is the simplest approach, as all users receive the same recommendations, potentially leading to 'popularity biases' and 'bandwagon effects' in which consumers gravitate toward already popular items [33]. 3. _Content-based filtering_: The main idea here is to create clusters of content and associate these clusters with user profiles [51]. Content-based filtering tries to recommend items similar to those a given user has liked in the past based on similarity scores of a user toward all the items [53]. 4. _Hybrid approaches_: Often, news recommender systems use a hybrid approach combining content-based filtering and collaborative filtering ([PERSON] et al., 2018), also including other methods such as weighing items by recency or pushing content that has specific features (e.g., paid content) [49]. The aforementioned types describe data-driven algorithms, but most news recommender systems employ additional rules which basically shape the overall design of the system. In most cases, these rules are jointly decided by the engineers and the editorial  
Figure 1: The proposed model incorporates baseline NRS data flows and processes, elaborating on the civic role of journalism (reddish routes) through novel metrics to make the underlying mechanisms more robust and useful. team of the news organization [16]. So, while in rule-based systems, the automated process is carefully designed to reflect the choices and the criteria that are set by the creators; in data-driven systems, algorithmic bias is less direct, as it is caused by the attributes of the available data that are used to build the decision-making model through the algorithmic process. When designing NRSs, several issues need to be taken under consideration. Most recommender algorithms are based on a topic-centered approach aiming to meet the different interests of users [54]. However, this approach can prove ineffective considering the specific nature of news items compared to other media offerings [55]. To begin with, news classification (tagging) is a difficult task, as news items may belong to more than one news category. Most often, news organizations follow their own typology of tagging and classifying news stories, which is a practice with a substantial degree of subjectivity. Additionally, most news items have a short lifespan, and thus, it is necessary to process them as fast as possible and start recommending them because their information value degrades [51].