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A data-driven approach to online fitting services
University of Borås, Faculty of Librarianship, Information, Education and IT. (Strategic Research Program in Data Science)ORCID iD: 0000-0003-0274-9026
University of Borås, Faculty of Librarianship, Information, Education and IT. (Strategic Research Program in Data Science)
University of Borås, Faculty of Librarianship, Information, Education and IT.
2018 (English)In: Data Science and Knowledge Engineering for Sensing Decision Support / [ed] Jun Liu (Ulster University, UK), Jie Lu (University of Technology Sydney, Australia), Yang Xu (Southwest Jiaotong University, China), Luis Martinez (University of Jaén, Spain) and Etienne E Kerre (University of Ghent, Belgium), 2018, p. 1559-1566Conference paper, Published paper (Refereed)
Abstract [en]

Being able to accurately predict several attributes related to size is vital for services supporting online fitting. In this paper, we investigate a data-driven approach, while comparing two different supervised modeling techniques for predictive regression; standard multiple linear regression and neural networks. Using a fairly large, publicly available, data set of high quality, the main results are somewhat discouraging. Specifically, it is questionable whether key attributes like sleeve length, neck size, waist and chest can be modeled accurately enough using easily accessible input variables as sex, weight and height. This is despite the fact that several services online offer exactly this functionality. For this specific task, the results show that standard linear regression was as accurate as the potentially more powerful neural networks. Most importantly, comparing the predictions to reasonable levels for acceptable errors, it was found that an overwhelming majority of all instances had at least one attribute with an unacceptably high prediction error. In fact, if requiring that all variables are predicted with an acceptable accuracy, less than 5% of all instances met that criterion. Specifically, for females, the success rate was as low as 1.8%.

Place, publisher, year, edition, pages
2018. p. 1559-1566
Keywords [en]
Predictive regression, online fitting, fashion
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
URN: urn:nbn:se:hb:diva-15824DOI: 10.1142/11069OAI: oai:DiVA.org:hb-15824DiVA, id: diva2:1291447
Conference
13th International FLINS Conference, Belfast, August 21-24, 2018.
Projects
Datadriven innovation
Funder
Knowledge FoundationAvailable from: 2019-02-25 Created: 2019-02-25 Last updated: 2019-02-25Bibliographically approved

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Löfström, TuweJohansson, UlfBalkow, JennySundell, Håkan

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