Authors: A. Seewald 1 ; T. Wernbacher 2 ; A. Pfeiffer 2 ; N. Denk 2 ; M. Platzer 3 ; M. Berger 3 and T. Winter 4

Affiliations: 1 Seewald Solutions, Lärchenstraße 1, A-4616 Weißkirchen a.d. Traun and Austria ; 2 Donau-Universität Krems, Dr.-Karl-Dorrek-Straße 30, A-3500 Krems and Austria ; 3 yVerkehrsplanung, Brockmanngasse 55, A-8010 Graz and Austria ; 4 Attribu-i, Nibelungengasse 32d, A-8010 Graz and Austria

ISBN: 978-989-758-350-6

ISSN: 2184-433X

Keyword(s): Machine Learning, Visualization, Data Mining, Rule Learning, e-Commerce, Returned Goods.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Visualization

Abstract: The importance of e-commerce including the associated freight traffic with all its negative consequences (e.g. congestion, noise, emissions) is constantly increasing. Already in 2015, an European market volume of 444 billion Euros at an annual growth of 13.3% was achieved, of which clothing and footwear account for 12.7% as the largest category (Willemsen et al., 2016). However, online commerce will only have a better footprint than buying in the local retail shop under optimal conditions (for example: group orders, always present at home delivery, no returns and no same day delivery). Next to frequent single deliveries, CO2 intensive and underutilized transport systems, returned goods are the main problem of online shopping. The last is currently estimated at up to 50% (Hofacker and Langenberg, 2015; Kristensen et al., 2013). Our research project Think!First tackles these problems in freight mobility by using an unique combination of gamification elements, persuasive design principle s and machine learning. Customers are animated, targeted and nudged to choose effective and sustainable means of transport when shopping online while ensuring best fit by compensating both manufacturer and customer biases in body size estimation. Here we show preliminary results and also present a slightly modified rule learning algorithm that always characterizes a given class (here: returns). (More)

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Paper citation in several formats:
Seewald, A. K.; Wernbacher, T.; Pfeiffer, A.; Denk, N.; Platzer, M.; Berger, M. and Winter, T. (2019). Towards Minimizing e-Commerce Returns for Clothing.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, ISSN 2184-433X, pages 801-808. DOI: 10.5220/0007568508010808

author={Seewald, A. K. and T. Wernbacher. and A. Pfeiffer. and N. Denk. and M. Platzer. and M. Berger. and T. Winter.},
title={Towards Minimizing e-Commerce Returns for Clothing},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},


JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Towards Minimizing e-Commerce Returns for Clothing
SN - 978-989-758-350-6
AU - Seewald, A. K.
AU - Wernbacher, T.
AU - Pfeiffer, A.
AU - Denk, N.
AU - Platzer, M.
AU - Berger, M.
AU - Winter, T.
PY - 2019
SP - 801
EP - 808
DO - 10.5220/0007568508010808

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