Towards a news recommendation system to increase reader engagement through newsletter content personalization - Fernandes, E.; Moro, S.; Cortez, P. (Procedia Computer Science, 2024)
In the big data era, recommendation systems (RS) play a pivotal role to overcome information overload. In the digital landscape publishers need to optimize their editorial strategies to increase reader engagement and digital revenue. Newsletters emerged as an important conversion channel to engage readers as they provide a personalized experience by building habits. However, the lack of human resources and the need for more content assertiveness per reader lead publishers to search for an advanced analytics solution. We address this problem by proposing a research agenda on news recommendation algorithms inspired in the table d’hôte approach and the concept of ‘personalized diversity’. Thus, the reader receives a personalized newsletter where he can discover informative and surprising content. The goal is to offer a self-contained package that retains readers, increases loyalty and consequently, the propensity to subscribe. A live controlled experiment with readers from the Portuguese newspaper Público was performed and a new approach is proposed. We study the effects of content recommendations on the behavior of newsletter subscribers. Findings reveal that serendipitous content tends to increase reader engagement. Finally, we propose a table d’hôte approach and new challenges are identified for future research.