Personalized Fashion Recommendation via Deep Personality Learning
Dongmei Mo (The Hong Kong Polytechnic University),* Xingxing Zou (Laboratory for Artificial Intelligence in Design, The Hong Kong Polytechnic University), Waikeung Wong (Institute of Textiles and Clothing, The Hong Kong Polytechnic University)
The 34th British Machine Vision Conference
Abstract
Fashion personality can help individuals to identify the essence of their styles and make better style decisions. This paper integrates user personality with physical attributes for fashion recommendation. The proposed personality learning model (P-Net) integrates user characteristics, including personality and personal physical information (such as skin color, hair color, etc.) with fashion styles for personalized recommendation. P-Net first learns outfit embeddings via a feature encoder, and the embeddings are then fed to a message-passing network to model the relations among different outfits. The personality-style learning module learns the fashion personalities of the users, and the physical compatibility is learned by exploring the relations of the feature embeddings and the physical attributes via a Transformer module. Qualitative and quantitative results on a new stylish outfit of personality (SOP) dataset indicate the superiority of P-Net compared with state-of-the-art methods and discover the potential of the combination of fashion aesthetics and psychological science.Video
Citation
@inproceedings{Mo_2023_BMVC,
author = {Dongmei Mo and Xingxing Zou and Waikeung Wong},
title = {Personalized Fashion Recommendation via Deep Personality Learning},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0385.pdf}
}
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