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Using Web Data to Reveal 22-Year History of Sneaker Designs

Park, Sungkyu / Song, Hyeonho / Han, Sungwon / Weldegebriel, Berhane / Manovich, Lev / Arielli, Emanuele / Cha, Meeyoung

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Abstract

Web data and computational models can play important roles in analyzing cultural trends. The current study presents an analysis of 23,492 sneaker images and metadata collected from a global reselling shop, StockX.com. Based on data encompassing 22 years from 1999 to 2020, we propose a sneaker design index that helps track changes in the design characteristics of sneakers using a contrastive learning method. Our data suggest that sneaker designs have been employing brighter colors and lower hue and saturation values over time. We also observe how popular brands have continued to build their unique identities in shape-related design space. The embedding analysis also predicts which sneakers will likely see a high premium in the reselling market, suggesting viable algorithm-driven investment and design strategies. The current work is one of the first publicly available studies to analyze product design evolution over a long historical period and has implications for the novel use of Web data to understand cultural patterns that are otherwise difficult to assess.

Issue Date
2022-04
Publisher
Association for Computing Machinery
URI
https://archives.kdischool.ac.kr/handle/11125/55003
10.1145/3485447.3512017
URL
https://archives.iw3c2.org/www2022/main-proceedings/
Conf. Name
WWW '22: Proceedings of the ACM Web Conference 2022
Place
FR
Conference Date
2022-04-05
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