Not One News Recommender To Fit Them All: How Different Recommender Strategies Serve Various User Segments

Many news recommender systems (NRS) adopt a one-recommender-for-all approach, overlooking that users engage with news in fundamentally different ways. In this work, we identify user clusters based on various engagement metrics that go beyond clicks by employing cluster analysis on two real-world datasets: EB-NeRD and Adressa. Next to that, we evaluate the performance of common rec-ommender strategies: popularity, collaborative filtering (EASE and ItemKNN), and a content-based model across these user clusters, which exhibit varying reading behaviors and information needs. Our findings show that different recommender strategies are effective to varying degrees depending on the user cluster. This study contributes to NRS research by providing a grounded clustering of users derived from real-world datasets and emphasizes the importance of user-centered evaluations for understanding how NRS strategies serve audiences with varying levels of news engagement.