Abstract Most of the existing personalization systems such as content recommenders or targeted ads focus on individual users and ignore the social situation in which the services are consumed. However, many human activities are social and involve several individuals whose tastes and expectations must be taken into account by the system. When a group profile is not available, different profile aggregation strategies can be applied to recommend adequate items to a group of users based on their individual profiles. We consider an approach intended to determine the factors that influence the choice of an aggregation strategy. We present evaluations made on a large-scale dataset of TV viewings, where real group interests are compared to the predictions obtained by combining individual user profiles according to different strategies.