  Recommender Systems are a subclass of information retrieval systems, or more
succinctly, a class of information filtering systems that seeks to predict how
close is the match of the user's preference to a recommended item. A common
approach for making recommendations for a user group is to extend Personalized
Recommender Systems' capability. This approach gives the impression that group
recommendations are retrofits of the Personalized Recommender Systems.
Moreover, such an approach not taken the dynamics of group emotion and
individual emotion into the consideration in making top_N recommendations.
Recommending items to a group of two or more users has certainly raised unique
challenges in group behaviors that influence group decision-making that
researchers only partially understand. This study applies the Affective Aware
Pseudo Association Method in studying group formation and dynamics in group
decision-making. The method shows its adaptability to group's moods change when
making recommendations.
