In many domains, it is often required to provide recommendations for groups, instead of individual users. Existing approaches try to compensate for the lack of group profiles, by either merging individual profiles, or treating users separately and then fusing the recommendations. Both paradigms thus fail to account for the different roles and behaviors people assume when making group decisions. In this work, we propose two novel group recommendation models that explicitly try to model the behavior of group members and distinguish it from that when they act alone. A detailed evaluation has shown that our models consistently provide significantly better recommendations. In addition, useful conclusions are drawn regarding the favorable settings of existing techniques.