基于深度学习的纺织产品在线推荐系统

Ümit Turkut, Adem Tuncer, Hüseyin Savran, Sait Yilmaz
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引用次数: 8

摘要

近年来,为了确保客户满意度和加速销售,推荐系统经常成为首选。由于这些系统,它旨在加快客户的决策过程。推荐系统已经成为必不可少的一部分,尤其是在网上购物中。最近,在许多不同领域使用的大多数推荐系统都引起了人们的注意,主要集中在时尚和服装上。本文利用卷积神经网络(CNN)提出了一种基于深度学习的在线推荐系统。CNN架构中不同模式的类别是根据用户和设计者的模式偏好来确定的。深度学习模型根据纺织品的颜色兼容性推荐图案。所提出的模型已经使用我们自己的模式数据集(包括12000张图像)进行了训练和测试。在模式数据集上的实验证明了该方法的有效性。
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An Online Recommendation System Using Deep Learning for Textile Products
Recommendation systems are frequently preferred in recent years ensuring customer satisfaction and accelerating sales. Thanks to these systems, it is aimed to accelerate the decision-making process of customers. Recommendation systems have become a necessary part, especially in online shopping. Most of the recommendation systems used in many different areas have been attracting attention, focusing on fashion, and clothing recently. In this paper, a deep learning-based online recommendation system has been proposed with a Convolutional Neural Network (CNN). Classes of different patterns in the CNN architecture have been determined according to users' and designers' pattern preferences. The deep learning model recommends patterns considering color compatibility for textile products. The proposed model has been trained and tested using our own pattern dataset including 12000 images. Experiments on pattern datasets show the effectiveness of our proposed approach.
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