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

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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.
Publisher
Association for Computing Machinery, Inc
Issue Date
2022-04
Language
English
Citation

31st ACM World Wide Web Conference, WWW 2022, pp.2967 - 2977

DOI
10.1145/3485447.3512017
URI
http://hdl.handle.net/10203/298884
Appears in Collection
CS-Conference Papers(학술회의논문)
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