Climate-Friendly Online Shopping Within the Green eCommerce Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth Sensing

Alexander Seewald, Thomas Wernbacher, Thomas Winter, Mario Platzer, Alexander Pfeiffer

2024

Abstract

Within the context of the Green eCommerce project where we build tailored add-ons for webshops to increase climate-friendly shipping, we analyzed reasons for returns using a modified rule learning algorithm but found no actionable rules. However, since many returns are driven by wrong size information, we have also developed a prototype Fitting Tool app that uses active depth sensing to measure several relevant body measurements and uses these to estimate T-Shirt sizes. Although these body measurements could be shown to be quite precise, T-Shirt sizes could only be predicted at low accuracy. On the other hand, self-reporting by test users showed that the perceived accuracy was considered 1.5-3x higher. Analyzing this issue, it was found that the reason for this is most likely manufacturer bias in reported size, which will be addressed in future work.

Paper Citation

in Harvard Style

Seewald A., Wernbacher T., Winter T., Platzer M. and Pfeiffer A. (2024). Climate-Friendly Online Shopping Within the Green eCommerce Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth Sensing. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 920-927. DOI: 10.5220/0012419500003636

in Bibtex Style

@conference{icaart24,
author={Alexander Seewald and Thomas Wernbacher and Thomas Winter and Mario Platzer and Alexander Pfeiffer},
title={Climate-Friendly Online Shopping Within the Green eCommerce Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth Sensing},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={920-927},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012419500003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Climate-Friendly Online Shopping Within the Green eCommerce Project: A Fitting Tool to Determine T-Shirt Sizes Using Active Depth Sensing
SN - 978-989-758-680-4
AU - Seewald A.
AU - Wernbacher T.
AU - Winter T.
AU - Platzer M.
AU - Pfeiffer A.
PY - 2024
SP - 920
EP - 927
DO - 10.5220/0012419500003636
PB - SciTePress