Enabling personalized VR experiences: a framework for real-time adaptation and recommendations in VR environments

Abstract

The personalization of user experiences through recommendation systems has been extensively explored in Internet applications, but this has yet to be fully addressed in Virtual Reality (VR) environments. The complexity of managing geometric 3D data, computational load, and natural interactions poses significant challenges in real-time adaptation in these immersive experiences. However, tailoring VR environments to individual user needs and interests holds promise for enhancing user experiences. In this paper, we present Virtual Reality Environment Adaptation through Recommendations (VR-EAR), a framework designed to address this challenge. VR-EAR employs customizable object metadata and a hybrid recommendation system modeling implicit user feedback in VR environments. We utilize VR optimization techniques to ensure efficient performance. To evaluate our framework, we designed a virtual store where product locations dynamically adjust based on user interactions. Our results demonstrate the effectiveness of VR-EAR in adapting and personalizing VR environments in real time. domains.