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基于循环神经网络模型的在线购物满意度的动态评估分析

Cheng Zhao¹, Yi Xun²

¹School of Art & Design, Guangdong University of Technology, Guangzhou, 510000, China.

Scientific reports

|July 2, 2025

概括

本研究介绍了一种动态加权GRU模型,用于预测在线购物满意度,其性能优于传统的循环神经网络. 该模型增强了用户满意度预测和产品建议.

科学领域:

人工智能的人工智能
机器学习机器学习
电子商务分析电子商务分析

背景情况:

在线购物满意度对于平台开发和用户保留至关重要.
循环神经网络 (RNN) 用于序列数据,但与长序列和消失梯度作斗争.
门式循环单位 (GRU) 通过捕获长期依赖来改善RNN.

研究的目的:

开发一种先进的模型,准确预测用户在网上购物中的满意度.
解决传统RNN在处理长期依赖和消失梯度方面的局限性.
为在电子商务平台上增强用户体验和产品推提供技术支持.

主要方法:

实施动态加权GRU (DW-GRU) 模型,对GRU进行增强.
DW-GRU包含了一个动态权重机制,以适应不断变化的用户满意度.
模型评估使用亚马逊购物数据集进行行为预测.

主要成果:

与标准RNN相比,DW-GRU模型表现出更高的性能.
在满意度预测中实现了高精度 (0.871),精度 (0.667) 和回忆 (0.667).
有效地捕获了长期的依赖关系,并减轻了消失梯度的问题.

结论:

DW-GRU模型为预测网上购物用户满意度提供了一个强大的解决方案.
调查结果为电子商务运营商和数据分析师提供了宝贵的见解.
该研究支持通过改进的行为预测来提高用户满意度和准确的产品建议.

关键词:

人工智能的人工智能是人工智能.动态评估是动态的评估.在线购物网上购物经常性的神经网络.满意度是一种满足感.

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