Mendeley Suggest is a recommender system that helps researchers keep up to date with articles in their field and fill gaps in their knowledge. It leverages usage data from the Mendeley reference management system, in which millions of users have compiled personal libraries of relevant articles. In this chapter, we explain how recommendations are generated using user-based collaborative filtering. Much of the development of the live system has focused on keeping users engaged as they repeatedly interact with the system and adapting to their research interests as they add new articles to their libraries. The product has evolved over time through taking into account user feedback, experimenting with new approaches, and making data-driven decisions. As Mendeley Suggest continues to develop, we expect to incorporate additional domain-specific features to supplement collaborative filtering and provide even more useful recommendations.