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Recommender Systems are a subclass of information retrieval systems, or moresuccinctly, a class of information filtering systems that seeks to predict howclose is the match of the user's preference to a recommended item. A commonapproach for making recommendations for a user group is to extend PersonalizedRecommender Systems' capability. This approach gives the impression that grouprecommendations are retrofits of the Personalized Recommender Systems.Moreover, such an approach not taken the dynamics of group emotion andindividual emotion into the consideration in making top_N recommendations.Recommending items to a group of two or more users has certainly raised uniquechallenges in group behaviors that influence group decision-making thatresearchers only partially understand. This study applies the Affective AwarePseudo Association Method in studying group formation and dynamics in groupdecision-making. The method shows its adaptability to group's moods change whenmaking recommendations.

international journal on natural language computing (print)/international journal on natural language computing

Applying the Affective Aware Pseudo Association Method to Enhance the  Top-N Recommendations Distribution to Users in Group Emotion Recommender  Systems