Recommender systems addvalue to vast content resources by matching users with items of interest. In recent years, immense progress has been made in recommendation techniques. The evaluation of these systems is still based on traditional information retrieval and statistics metrics (e.g., precision, recall, RMSE), often not taking the use case and situation of the system into consideration. However, the rapid evolution of recommender systems in both their goals and their application domains fosters the need for new evaluation methodologies and environments. This special issue serves as a venue for work on novel, recommendation-centric benchmarking approaches taking the users’ utility, the business values, and the technical constraints into consideration.