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

TL;DR

This paper introduces an affective-aware method to improve group-based Top-N recommendations by considering group and individual emotions, addressing challenges in group decision-making dynamics.

Contribution

It applies the Affective Aware Pseudo Association Method to enhance understanding of group emotions in recommender systems, improving recommendation relevance.

Findings

Method adapts to group mood changes during recommendations

Improves accuracy of group Top-N recommendations

Addresses emotional dynamics in group decision-making

Abstract

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.