AFTER: Adaptive Friend Discovery for Temporal-Spatial and Social-Aware XR

Recent advancements in the field of extended reality (XR) have garnered significant interest in XR socialization. However, traditional social XR experiences often fall short of satisfying users' social expectations due to the negligence of the emerging opportunities in XR. In this paper, we propose a novel scenario of socializing in social XR, which has the potential to substantially enhance traditional social media through i) the recommendation of appropriate surrounding users that cater to users' individual preferences, ii) the adaptive avoidance of view occlusions to facilitate users in locating their friends, iii) the consideration of users' social presence, and iv) the development of cross-platform solutions to provide hybrid participation. To this end, we formulate Adaptive Friend Discovery for Temporal-spatial and Social-aware XR, a new NP-hard social recommendation problem aiming at satisfying social XR users. The proposed model, POSHGNN, is a deep temporal graph learning framework designed to provide efficient social recommendations for target users. Experimental results obtained from real-world social XR datasets and a user study that supports multiple XR interfaces demonstrate that the proposed method outperforms baseline approaches with an improvement of 18.5 % in solution quality.