Post-it: Augmented Reality Based Group Recommendation with Item Replacement.

AR shopping has attracted significant attention with the emergence of popular AR applications, e.g., IKEA Place and Nike AR Fit. However, most AR stores merely provide auxiliary information for each user, instead of group recommendations that leverage new AR features. In this paper, we make the first attempt to explore AR shopping to simultaneously: i) leverage flexible display and item replacement to maximize preference, ii) construct an immersive environment with virtual item haptic feedback, and iii) stimulate social interactions with common user interests. We formulate the Tangible AR Group Shopping (TARGS) problem to partition users and recommend virtual substitutes. We then develop the Social-aware Tangible AR Replacement and Recommendation (STAR3) system, including a connected graph neural network for modeling graph features from different domains, a virtual-physical item mapping model to enable haptic experience by leveraging on-shelf items’ passive haptic feedback, and a multi-goal recommender to dynamically split user groups and recommend substitutes for satisfaction maximization. Experimental results manifest that STAR3 outperforms baselines by 23 % to 63 % in three real-world datasets and a user study.