Proceedings of the ACM Web Conference 2022
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 algorithmdriven 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.
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Chen et al. automatically decompose each relation label into its constituent words and use them as an answer.
“When performing relation extraction, Chen et al. [13] automatically decompose each relation label into its constituent words and use them as an answer.”
From Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2023) · cites this paper as [13]
In relation extraction, different tokens in the input sentence may be more or less important, which cannot be easily reflected in the prompt templates for classification.
“(2) In relation extraction, different tokens in the input sentence may be more or less important (e.g., entity mentions are more likely to participate in a relation), which, however, cannot be easily reflected in the prompt templates for classification, since the original prompt template regards each word equally.”
From Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2023) · cites this paper as [13]
In relation extraction, the larger label space results in more difficulty in prompt answer engineering.
“Chen et al. [13] first explored the application of fixed-prompt LM Tuning in relation extraction and discuss two major challenges that hinder the direct inheritance of prompting methodology from classification tasks: (1) The larger label space (e.g., 80 in relation extraction vs. 2 in binary sentiment classification) results in more difficulty in prompt answer engineering.”
From Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2023) · cites this paper as [13]
Existing works make a step by making prompts with additional marks to encode lexical information, such as entity markings.
“Existing works [13] make a step by making prompts with additional marks to encode lexical information, such as entity markings.”
From Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2023) · cites this paper as [13]
Cites
Representation Learning with Contrastive Predictive Coding
Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242 · see citing works
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Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works
Pedreschi, D., Pappalardo, L., Ferragina, E., Baeza-Yates, R., Barabási, A.-L., Dignum, F., Dignum, V., Eliassi-Rad, T., Giannotti, F., Kertész, J., et al. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2024.104244 · Cited by 102 · see citing works
Zeng, H., Luo, C., & Zamani, H. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657746 · Cited by 22 · see citing works
- Authors
- Laforest, F., Raphaël Troncy, A., Simperl, E. P. B., Agarwal, D., Gionis, A., Herman, I., & Médini, L.
- Affiliations
- Traces (FR)
- EURECOM (FR)
- Publisher
- ACM
- Published
- 2022-04-25
- OpenAlex
- W4224307896
- Cited by
- 522
- References
- 1
- Citations by year
- 2025: 255 · 2024: 168 · 2023: 90 · 2022: 9
- Type
- paratext
- Language
- en
- Open access
- closed
- Locations
- HAL (submittedVersion)
- Funders
- National Research Foundation (IBS-R029-C2)
- National Research Foundation of Korea (NRF-2017R1E1A1A01076400, 2017R1E1A1A01076400)
- Institute for Basic Science (IBS-R029-C2)
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