基于深度学习和非线性神经网络回归的服装销售预测

Chandadevi Giri, S. Thomassey, Jenny Balkow, Xianyi Zeng
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引用次数: 10

摘要

与其他零售行业相比,时装零售业在预测其产品未来需求方面面临着许多挑战。这是由于消费者的选择不断变化,他们受到快速变化的市场趋势的影响,这导致时尚产品的生命周期很短。由于电子商务商业模式的出现,时装零售商不得不在其网站上放置大量的虚拟产品图像以及其特色信息,以便客户了解时装产品,提高他们的购买体验。时装零售商必须提前预测未来消费者的偏好;然而,他们缺乏先进的工具来实现这一目标。为了克服这一问题,本研究工作使用深度学习将产品的历史信息与图像特征相结合,并预测未来的销售。将服装图像转换成特征向量,然后与历史销售数据进行合并。应用反向传播神经网络模型对新产品的销售进行预测。结果表明,尽管数据集很小,但该模型的性能仍然很好。这种方法有望预测市场上新上市的服装,时尚零售商可以提高效率和增长。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting New Apparel Sales Using Deep Learning and Nonlinear Neural Network Regression
Compared to other retail industries, fashion retail industry faces many challenges to foresee future demand of its products. This is due to ever-changing choices of their consumers, who get influenced by rapidly changing market trends and it leads to the short life cycle of a fashion product. Due to the advent of e-commerce business models, fashion retailers have to put a multitude of virtual product images along with their feature information on their websites in order for their customers to know the fashion products and improve their purchasing experience. It is imperative for fashion retailers to predict future consumer preferences in advance; however, they lack advanced tools to achieve this goal. To overcome this problem, this research work combines the historical information of products with their image features using deep learning and predicts future sales. Apparel images are converted into feature vectors and then are merged with historical sales data. We applied backward propagation neural network model to predict the sales of a new product. It is found that the model performs quite well despite the small size of the dataset. This approach could be promising for forecasting the new arrivals of apparels in the market, and fashion retailers could improve their efficiency and growth.
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