Four-Dimensional Shopping Mall: Sequential Group Willingness Optimization under VR Environments

Hong-Han Shuai, Yueh-Hsue Li, Chun-Chieh Feng, Wen-Chih Peng
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引用次数: 5

Abstract

Nowadays, people get used to buying a variety of commodities from e-commerce platforms because of the convenience and easy access to Internet. As a result, the sales on e-commerce platforms have grown exponentially. It is promising to customize the online shops for different users under VR environments since users do not need to waste time of going upstairs or downstairs for finding interesting commodities. Therefore, in this demo paper, we propose a novel four-dimensional shopping mall that offers online group shopping services with the customized shop recommendation, where a group of friends in the proposed four-dimensional shopping mall can teleport to the next shop by one click. We formulate a Sequential group wIllingness OptimizatioN (SION) problem, prove SION is NP-hard, and provide an efficient algorithm called ζ-GSS. The experimental results show that the solution quality of the proposed ζ-GSS is close to the optimal solution, while the execution time only requires 3.3%. Finally, we build the prototype of the four-dimensional shopping mall, which can be demonstrated for users to experience the next-generation online shopping.
四维购物中心:VR环境下的序贯群体意愿优化
如今,人们习惯于从电子商务平台上购买各种各样的商品,因为互联网的方便和容易访问。因此,电子商务平台上的销售额呈指数级增长。在VR环境下,为不同的用户定制网上商店是有希望的,因为用户不需要浪费时间上楼或下楼去寻找感兴趣的商品。因此,在本文的演示中,我们提出了一种新颖的四维购物中心,提供在线团购服务,并提供定制的商店推荐,在所提出的四维购物中心中,一群朋友可以一键传送到下一个商店。我们提出了一个序列群体意愿优化(SION)问题,证明了SION是np困难的,并给出了一种称为ζ-GSS的有效算法。实验结果表明,所提出的ζ-GSS的解质量接近最优解,而执行时间仅需要3.3%。最后,我们构建了四维购物中心的原型,可以演示给用户体验下一代的网上购物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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