Modeling and Validating a News Recommender Algorithm in a Mainstream Medium-Sized News Organization: An Experimental Approach

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.