Intelligent group recommendations for metaverse e-commerce platforms for enhanced retail and consumer experiences

Abstract

The advent of immersive social platforms introduces new challenges and opportunities for computational modeling of group dynamics and personalization of metaverse commerce. This research proposes an intelligent group recommender system (GRS) algorithm that analyze collective user behaviors and preferences to enhance customer shopping experiences. The proposed GRS integrates demographic-based clustering to determine user groups and then aggregates their preferences to generate tailored recommendations. We conduct a simulation case study to demonstrate the applicability of the proposed approach. The results show the GRS identifies heterogeneity within clusters based on demographics and product preferences. Our findings reveal that the integrated GRS in a metaverse commerce platform not only enhances the retailing experience by accurately matching products with group preferences but also provides actionable intelligence for businesses in crafting targeted strategies. This study advances the emerging field of intelligent recommender systems by integrating group modelling, preference aggregation, and immersive technologies to enable next-generation personalization and automation in e-commerce platforms.