Typical recommender evaluations treat users as an homogeneous unit. However, user subgroups often differ in their tastes, which can result more broadly in diverse recommender needs. Thus, these groups may have different degrees of satisfaction with the provided recommendations. We explore the offline top-N performance of collaborative filtering algorithms across two domains.
We find that several strategies achieve higher accuracy for dominant demographic groups, thus increasing the overall performance for the strategy, without providing increased benefits for other users.