Personalized Clothing Prediction Algorithm Based on Multi-modal Feature Fusion
With the popularization of information technology and the improvement of material living standards, fashion consumers are faced with the daunting challenge of making informed choices from massive amounts of data. This study aims to propose deep learning technology and sales data to analyze the per...
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Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
Taiwan Association of Engineering and Technology Innovation.
2024
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Subjects: | |
Online Access: | http://ir.unimas.my/id/eprint/44547/1/Personalized%20Clothing%20Prediction%20Algorithm.pdf http://ir.unimas.my/id/eprint/44547/ https://ojs.imeti.org/index.php/IJETI/article/view/13394 https://doi.org/10.46604/ijeti.2024.13394 |
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Institution: | Universiti Malaysia Sarawak |
Language: | English |
Summary: | With the popularization of information technology and the improvement of material living standards, fashion
consumers are faced with the daunting challenge of making informed choices from massive amounts of data. This
study aims to propose deep learning technology and sales data to analyze the personalized preference characteristics
of fashion consumers and predict fashion clothing categories, thus empowering consumers to make well-informed
decisions. The Visuelle’s dataset includes 5,355 apparel products and 45 MB of sales data, and it encompasses image
data, text attributes, and time series data. The paper proposes a novel 1DCNN-2DCNN deep convolutional neural
network model for the multi-modal fusion of clothing images and sales text data. The experimental findings exhibit
the remarkable performance of the proposed model, with accuracy, recall, F1 score, macro average, and weighted
average metrics achieving 99.59%, 99.60%, 98.01%, 98.04%, and 98.00%, respectively. Analysis of four hybrid
models highlights the superiority of this model in addressing personalized preferences |
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