DeepStore: Understanding Customer Behaviors in Unmanned Stores

Bin Guo; Ziqi Wang; Pei Wang; Tong Xin; Daqing Zhang; Zhiwen Yu

doi:10.1109/MITP.2019.2928272

DeepStore: Understanding Customer Behaviors in Unmanned Stores

Bin Guo, Ziqi Wang, Pei Wang, Tong Xin, Daqing Zhang, Zhiwen Yu

School of Computer Science

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

In recent years, we have witnessed a surge in new retail, which aims to combine the best of physical and online retailing using Internet of things and artificial intelligence techniques. The unmanned store is a representative type of new retail, which leverages wireless sensing and machine learning techniques to recognize fine-grained in-store customer behaviors, infer their intents, and learn their preferences. This paper gives an overview of this emerging research area, presents its key techniques and applications, and discusses the open issues of this field.

Original language	English
Article number	9098002
Pages (from-to)	55-63
Number of pages	9
Journal	IT Professional
Volume	22
Issue number	3
DOIs	https://doi.org/10.1109/MITP.2019.2928272
State	Published - 1 May 2020

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10.1109/MITP.2019.2928272

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abstract = "In recent years, we have witnessed a surge in new retail, which aims to combine the best of physical and online retailing using Internet of things and artificial intelligence techniques. The unmanned store is a representative type of new retail, which leverages wireless sensing and machine learning techniques to recognize fine-grained in-store customer behaviors, infer their intents, and learn their preferences. This paper gives an overview of this emerging research area, presents its key techniques and applications, and discusses the open issues of this field.",

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DeepStore: Understanding Customer Behaviors in Unmanned Stores

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