论文标题
优化社会意识VR购物的项目和子组配置
Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping
论文作者
论文摘要
在VR购物中心购物被认为是电子商务的范式转变，但是大多数传统的VR购物平台都是为单个用户设计的。在本文中，我们设想了VR集团购物的场景，该场景比在实体店中的传统集团购物和网络购物带来了很大的优势：1）配置灵活的显示物品和分区亚组的分区，以解决该组中的个人利益，以及2）支持子组中的社交互动以增强销售。因此，我们制定了社交感知的VR组项目配置（SVGIC）问题，以配置一组显示的项目，以灵活地分区的VR组购物中的用户分区子组。我们证明SVGIC在$ \ frac {32} {31} - ε$之内近似NP -HARD。我们根据共同播放子组形成（CSF）的概念设计了一种近似算法，以将适当的项目配置为与朋友的不同子组一起显示。对实际VR数据集和HTC Vive的用户研究的实验结果表明，我们的算法的表现优于基线的方法至少占解决方案质量的30.1％。
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 $\frac{32}{31} - ε$. 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.
