基于多模态特征融合的个性化服装预测算法

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY
Rong Liu, Annie Anak, Miaomiao Xin, Hongyan Zang, Wanzhen Wang, Shengqun Zhang
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引用次数: 0

摘要

随着信息技术的普及和物质生活水平的提高,时尚消费者面临着从海量数据中做出明智选择的艰巨挑战。本研究旨在通过深度学习技术和销售数据,分析时尚消费者的个性化偏好特征,预测时尚服饰品类,从而帮助消费者做出明智的决策。Visuelle 的数据集包括 5,355 种服装产品和 45 MB 的销售数据,其中包含图像数据、文本属性和时间序列数据。本文提出了一种新颖的 1DCNN-2DCNN 深度卷积神经网络模型,用于服装图像和销售文本数据的多模态融合。实验结果表明,该模型的准确率、召回率、F1 分数、宏观平均值和加权平均值分别达到了 99.59%、99.60%、98.01%、98.04% 和 98.00%。对四种混合模型的分析凸显了该模型在处理个性化偏好方面的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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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来源期刊
CiteScore
2.80
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊介绍: The IJETI journal focus on the field of engineering and technology Innovation. And it publishes original papers including but not limited to the following fields: Automation Engineering Civil Engineering Control Engineering Electric Engineering Electronic Engineering Green Technology Information Engineering Mechanical Engineering Material Engineering Mechatronics and Robotics Engineering Nanotechnology Optic Engineering Sport Science and Technology Innovation Management Other Engineering and Technology Related Topics.
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