Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2015

Abstract

Shopping behavior data are of great importance to understand the effectiveness of marketing and merchandising efforts. Online clothing stores are capable capturing customer shopping behavior by analyzing the click stream and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to identify comprehensive shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which items of clothes they pay attention to, and which items of clothes they usually match with. The intuition is that the phase readings of tags attached on desired items will demonstrate distinct yet stable patterns in the time-series when customers look at, pick up or turn over desired items. We design ShopMiner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of ShopMiner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from twoweek shopping-like data show that ShopMiner could achieve high accuracy and efficiency in customer shopping behavior identification.

Keywords

Shopping behavior, RFID, Backscatter communication

Discipline

Digital Communications and Networking | OS and Networks

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

First Page

113

Last Page

125

Identifier

10.1145/2809695.2809710

Publisher

ACM

City or Country

Seoul, South Korea

Additional URL

https://doi.org/10.1145/2809695.2809710

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