In this study, the face recognition technique is applied to recognize the user's identity. The watching-history of the recognized user is provided to the proposed group recommendation algorithm for recommending programs. Besides, the proposed group recommendation algorithm takes the members' interaction into account for providing more reasonable program list to the group. The concept is based on computation of correlation between the group and its members for predicting the members' weights. Finally, the unwatched programs' ratings can be predicted, and the recommended Top-N programs will be provided to the group. The group members' weights are continuously updated by the recommendation algorithm to obtain more accurate relationships of the group members. The proposed method can reach convergence status after approximately 10 recommendations.