1 research outputs found
Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping
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 . 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.Comment: 32 pages, 16 figures (41 subfigures). A shorter version of this paper
has been submitted to the 46th International Conference on Very Large
Databases (VLDB 2020); this is an expanded version containing supplementary
detail