一种基于一维卷积神经网络的企业服务需求分类方法,具有交叉损失和企业肖像
Haixia Zhou1, Jindong Chen1,2
1School of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, China.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一种新方法,1D-CNN-CrossEntropyLoss,用于对企业的服务质量需求进行分类,改进推系统. 拟议的模型达到72.44%的准确性,超过现有方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 推系统面临着各种企业用户需求和冷启动问题的挑战.
- 企业质量服务平台需要有效的需求分类来个性化服务.
研究的目的:
- 为企业用户提出和评估一种新的服务质量需求分类方法,1D-CNN-CrossEntropyLoss.
- 通过提高需求分类准确度,解决推系统中的冷启动问题.
主要方法:
- 开发了一种集成一维卷积神经网络 (1D-CNN) 与交叉损失的分类方法.
- 利用从企业质量服务平台获得的综合企业质量肖像标签和交易数据.
- 将拟议的1D-CNN-CrossEntropyLoss模型与XGBoost,SVM和后勤回归进行了比较.
主要成果:
- 1D-CNN-CrossEntropyLoss模型实现了最高的分类准确率,达到72.44%.
- 整合企业质量的肖像显著提高了1D-CNN-CrossEntropyLoss模型的准确性和回忆力.
- 企业质量肖像增强了企业质量服务需求的分类.
结论:
- 与传统模型相比,1D-CNN-CrossEntropyLoss方法在对企业质量服务需求的分类方面表现出了卓越的表现.
- 企业质量肖像是提高分类准确性和为MSME提供推服务的有价值特征.
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