Group recommendation has become an important task in information systems, which makes recommendations towards a group rather than an individual. This is due to the prevalence of group activities in people’s daily life. However, most group recommendation researchers focus on utility performance isolatedly, ignoring unfairness caused by popularity bias. In this paper, we propose a Mitigating Popularity Bias for Multi-View Group Recommendation (MPBGR). Specifically, we first detect the popularity bias using a discriminator and then enhance fairness on the item side. To avoid the utility decrease raised by enhancing fairness, we further consider the collaborative relationships between users and items across different groups as well as group-to-group interactions. We conduct extensive experiments and the experimental results show that the proposed model outperforms the state-of-the-art approaches in balancing utility and fairness.