## 6 Conclusions  
This work attempted first to synthesize relevant work and propose a generic news recommendation system which goes beyond accuracy and considers the civic role of journalism for an informed citizenry. Second, it attempted to examine the application of a recommender system to a national-level mainstream news organization in a small European country, which was a decision driven by the participation of the medium in a funded research project. Regarding the first research question exploring the main differences between the Editor's Agenda and the algorithmic agenda, the study shows that the Editor's Agenda was based on a balanced mix of hard and soft stories aiming to cater for the diverse needs of a general audience. On the other hand, a large number of recommendations offered in the MyNews sessions were significantly influenced by the available pool of stories produced on a daily basis. In other words, the news diet offered in the MyNews area is shaped by an algorithmic logic as opposed to an editorial logic identified in the Editor's Agenda [66]. Thorough examination of the data provided by users specifically instructed to only view certain types of articles (_collaborating users_) and _ordinary users_ instructed to consume news stories on their own will and interests suggests that the application of the recommender system was problematic. While the algorithm does provide recommendations significantly different from what would be expected by chance, it ultimately fails to produce a personalized environment populated primarily by news items of the type that a user would be expected to view on the basis of their past behavior; this finding holds true for both _collaborating_ and _ordinary users_ examined here. A careful examination of the instances in which the algorithm fails the most suggests that this is the result of design flaws rooted in problematic rules. Our findings provide empirical evidence showing [48] that unintended side effects of design decisions undermine the accuracy capacity of algorithms. These design flaws can be summarized as follows:  
1. Voracity: We introduce the term voracity to refer to the large number of news stories expected to be offered in the MyNews area per user per session. Given the relatively small output of the news organization altogether, the identified under-performance of the algorithm was partly due to its being required to populate the personalized area with too many news items (28-32 stories). 2. Recency: The recency metric needs to be used with caution. In our case, it was mostly driven by the nature of the news content produced by the medium: timely, short-form stories aiming to build traffic. However, the requirement that all personalized recommendations must have been produced within a day of viewing--or preferably within the last four hours--significantly hampered the algorithm's capacity to provide accurate and relevant recommendations. 3. Unsystematic tagging: The classification and ranking of news items depends greatly on the tags assigned to news stories. Evidence of unsystematic tagging of articles had a negative impact on the algorithm's capacity to make accurate offerings. 4. Underuse of available content: Although the news portal under study is affiliated with four other websites, this content was excluded from the algorithm's repository. Considering the ambitious expectations of the MyNews area, designers and editors should provision a greater pool of content or limit the quantity of stories provided in the MyNews area. Finally, the decreasing engagement of users with the MyNews area can be associated not only with the problematic levels of accuracy but also with the random filling of the 30-items sessions--shaped primarily by the availability of content--as opposed to a more sophisticated algorithmic provision of recommendations including the principles of diversity, serendipity and novelty [33].