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Accommodating user diversity for in-store shopping behavior recognition

Published: 13 September 2014 Publication History

Abstract

This paper explores the possibility of using mobile sensing data to detect certain in-store shopping intentions or behaviours of shoppers. We propose a person-independent activity recognition technique called CROSDAC, which captures the diversity in the manifestation of such intentions or behaviours in a heterogeneous set of users in a data-driven manner via a 2-stage clustering-cum-classification technique. Using smartphone based sensor data (accelerometer, compass and Wi-Fi) from a directed, but real-life study involving 86 shopping episodes from 30 users in a mall's food court, we show that CROSDAC's mobile sensing-based approach can offer reasonably high accuracy (77:6% for a 2-class identification problem) and outperforms the traditional community-driven approaches that unquestioningly segment users on the basis of underlying demographic or lifestyle attributes.

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Cited By

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  • (2023)How in-store sensor technologies can help retailers to understand their customers: overview on two decades of researchThe International Review of Retail, Distribution and Consumer Research10.1080/09593969.2023.227325634:3(381-398)Online publication date: 30-Oct-2023
  • (2022)A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-ConceptInformation10.3390/info1308036313:8(363)Online publication date: 29-Jul-2022
  • (2021)Utilizing RFID Tag Motion Detection in High Tag Density Environments for Customer Browsing InsightsIEEE Journal of Radio Frequency Identification10.1109/JRFID.2021.30872295:4(345-356)Online publication date: Dec-2021
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cover image ACM Conferences
ISWC '14: Proceedings of the 2014 ACM International Symposium on Wearable Computers
September 2014
154 pages
ISBN:9781450329699
DOI:10.1145/2634317
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 September 2014

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Author Tags

  1. activity recognition
  2. mobile-sensing
  3. shopping behavior

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UbiComp '14
UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing
September 13 - 17, 2014
Washington, Seattle

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Overall Acceptance Rate 38 of 196 submissions, 19%

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Cited By

View all
  • (2023)How in-store sensor technologies can help retailers to understand their customers: overview on two decades of researchThe International Review of Retail, Distribution and Consumer Research10.1080/09593969.2023.227325634:3(381-398)Online publication date: 30-Oct-2023
  • (2022)A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-ConceptInformation10.3390/info1308036313:8(363)Online publication date: 29-Jul-2022
  • (2021)Utilizing RFID Tag Motion Detection in High Tag Density Environments for Customer Browsing InsightsIEEE Journal of Radio Frequency Identification10.1109/JRFID.2021.30872295:4(345-356)Online publication date: Dec-2021
  • (2020)Capturing Customer Browsing Insights through RFID Tag Motion Detection in High Tag Density Environments2020 IEEE International Conference on RFID (RFID)10.1109/RFID49298.2020.9244868(1-8)Online publication date: 28-Sep-2020
  • (2020)GroupShop: monitoring group shopping behavior in real world using mobile devicesJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01673-914:5(6367-6378)Online publication date: 4-Jan-2020
  • (2019)Digital Marketing through Physical Context Awareness2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE48274.2019.9162392(1-5)Online publication date: Dec-2019
  • (2019)Indoor Positioning Knowledge Model for Privacy Preserving Context-AwarenessService Research and Innovation10.1007/978-3-030-32242-7_5(50-64)Online publication date: 6-Oct-2019
  • (2018)Technology Is Transforming Shopping BehaviorMobile Commerce10.4018/978-1-5225-2599-8.ch071(1508-1529)Online publication date: 2018
  • (2018)I4SProceedings of the 2018 ACM International Symposium on Wearable Computers10.1145/3267242.3267259(156-159)Online publication date: 8-Oct-2018
  • (2017)Design and Implementation of an RFID-Based Customer Shopping Behavior Mining SystemIEEE/ACM Transactions on Networking10.1109/TNET.2017.268906325:4(2405-2418)Online publication date: 1-Aug-2017
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