Popularity Prediction for New and Unannounced Fashion Design Images

Danny W. L. Yu, Eric W. T. Ngai, Maggie C. M. Lee

2023

Abstract

People following the latest fashion trends gives importance to the popularity of fashion items. To estimate this popularity, we propose a model that comprises feature extraction using Inception v3 (a kind of Convolutional Neural Network) and a popularity score estimation using Multi-Layer Perceptron regression. The model is trained using datasets from Amazon (5,166 items) and Instagram (98,735 items) and evaluated by using mean-squared error, which is one of the many metrics of the performance of our model. Results show that, even with a simpler structure and requiring less input, our model is comparable with other more complicated methods. Our approach allows designers and manufacturers to predict the popularity of design drafts for fashion items, without exposing the unannounced design at social media or comparing with a large quantity of other items.

Paper Citation

in Harvard Style

Yu D., Ngai E. and Lee M. (2023). Popularity Prediction for New and Unannounced Fashion Design Images. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 729-736. DOI: 10.5220/0011768500003393

in Bibtex Style

@conference{icaart23,
author={Danny W. L. Yu and Eric W. T. Ngai and Maggie C. M. Lee},
title={Popularity Prediction for New and Unannounced Fashion Design Images},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={729-736},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011768500003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Popularity Prediction for New and Unannounced Fashion Design Images
SN - 978-989-758-623-1
AU - Yu D.
AU - Ngai E.
AU - Lee M.
PY - 2023
SP - 729
EP - 736
DO - 10.5220/0011768500003393