Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping

TL;DR

This paper introduces a novel approach for configuring items and subgroups in social-aware VR shopping, enhancing personalization and social interaction to improve sales, supported by theoretical analysis and experimental validation.

Contribution

It formulates the NP-hard SVGIC problem, proves its complexity, and proposes an approximation algorithm with demonstrated superior performance over baselines.

Findings

Algorithms outperform baselines by at least 30.1% in solution quality.

Proven NP-hardness of the SVGIC problem.

Experimental validation on real VR datasets and user study results.

Abstract

Shopping in VR malls has been regarded as a paradigm shift for E-commerce, but most of the conventional VR shopping platforms are designed for a single user. In this paper, we envisage a scenario of VR group shopping, which brings major advantages over conventional group shopping in brick-and-mortar stores and Web shopping: 1) configure flexible display of items and partitioning of subgroups to address individual interests in the group, and 2) support social interactions in the subgroups to boost sales. Accordingly, we formulate the Social-aware VR Group-Item Configuration (SVGIC) problem to configure a set of displayed items for flexibly partitioned subgroups of users in VR group shopping. We prove SVGIC is NP-hard to approximate within $\frac{32}{31} - \epsilon$. We design an approximation algorithm based on the idea of Co-display Subgroup Formation (CSF) to configure proper items for display to different subgroups of friends. Experimental results on real VR datasets and a user study with hTC VIVE manifest that our algorithms outperform baseline approaches by at least 30.1% of solution quality.