###### Abstract  
News recommending systems (NRSs) are algorithmic tools that filter incoming streams of information according to the users' preferences or point them to additional items of interest. In today's high-choice media environment, attention shifts easily between platforms and news sites and is greatly affected by algorithmic technologies; news personalization is increasingly used by news media to woo and retain users' attention and loyalty. The present study examines the implementation of a news recommender algorithm in a leading news media organization on the basis of observation of the recommender system's outputs. Drawing on an experimental design employing the 'algorithmic audit' method, and more specifically the 'collaborative audit' which entails utilizing users as testers of algorithmic systems, we analyze the composition of the personalized MyNews area in terms of accuracy and user engagement. Premised on the idea of algorithms being black boxes, the study has a two-fold aim: first, to identify the implicated design parameters enlightening the underlying functionality of the algorithm, and second, to evaluate in practice the NRS through the deployed experimentation. Results suggest that although the recommender algorithm manages to discriminate between different users on the basis of their past behavior, overall, it underperforms. We find that this is related to flawed design decisions rather than technical deficiencies. The study offers insights to guide the improvement of NRSs' design that both considers the production capabilities of the news organization and supports business goals, user demands and journalism's civic values.  
n 1 2022 202  
by news organizations is News Recommending Systems (NRSs). NRSs are algorithmic tools that filter incoming streams of information according to the users' preferences or point them to additional items of interest. Harnessing the deluge of big data and machine learning [8], these technical systems aggregate, filter, select and prioritize information [9]. The _Daily Me_ project foreseen by [PERSON] [10] in 1995 is becoming common practice as news publishers are increasingly experimenting with recommender systems to extent the provision of personalized news [11] hoping to increase their sites''stickiness', capture user data and reduce their dependence on external suppliers of such information [12].  
However, their job is not easy; in today's high-choice media environment, attention shifts easily between platforms and (news) sites [4] and is greatly affected by algorithmic technologies [9; 13]. More importantly, the task of recommending appropriate and relevant news stories to readers proves particularly challenging; in addition to the technical and design challenges associated with the news recommendations of most media offerings, personalized systems of news delivery present a special case given the impact of news for an informed citizenry [14; 15].  
Taking into consideration the business challenges of media organizations and the particularities of news content, the present study probes the algorithmic design of a news recommender introduced in the newsroom of a leading news portal in Cyprus. The study follows a design-oriented approach [16] aiming to identify the implicated parameters enlightening the underlying functionality of the algorithm and evaluate the NRS in hand to offer insights that can guide the improvement of NRS that support and align with business goals, user demands and journalism's civic values.