Apparel-based deep learning system design for apparel style recommendation
Apparel-based deep learning system design for apparel style recommendation
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DOI：
10.1108/ijcst-02-2018-0019
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发表时间：
2019-01-01
影响因子：
1.2
通讯作者：
Long, Yang
中科院分区：
文献类型：
--
作者：
Guan, Congying;Qin, Shengfeng;Long, Yang
服装推荐系统研究的最大挑战不是探索机器学习技术在时尚中的应用，而是真正了解服装、时尚和人，知道该学什么。本文的目的是探索一种先进的服装风格学习和推荐系统，可以认识到深设计相关的功能的衣服，并学习这些功能所传达的内涵意义有关的风格和身体，使它可以作为一个熟练的人类expert.Design/方法/方式提出建议这项研究首先提出了一种新的服装风格训练数据。其次，基于新提出的训练数据（包括属性、意义和原始图像数据）设计了三种智能服装学习模型，并比较了模型的性能，以确定最佳的学习模型。对于深度学习，引入了两种模型来训练预测模型，一种是卷积神经网络与基线分类器支持向量机联合，另一种是与新提出的分类器后期核融合。（平均预测率为88.1%）是第三种模型，该模型分为两步设计，一是通过服装图像预测服装属性，二是基于预测属性进一步预测服装意义。结果表明，增加建议的属性数据，捕捉服装设计的深层特征，提高模型的性能（例如从73.5%，模型B到86%，模型C），和服装推荐的新概念的基础上风格的含义是技术上applicable.Originality/value的服装数据和设计的三个训练模型最初介绍了这项研究。所提出的方法可以通过图像或设计属性来评估不同服装特征提取方法的优缺点，并在最新的CNN和传统的SVM之间平衡不同的机器学习技术。
Purpose The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert.Design/methodology/approach This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models' performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion.Findings The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable.Originality/value The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.