电子商务商品原材料的自动分类方法通过引入自我监督的概念和构建域本体学
Bing Lei1,2, Jinghua Wang3, Cong Shen4
1School of Management, Henan University of Technology, Zhengzhou, 450001, China.
Scientific reports
|February 10, 2026
概括
本研究引入了一种新的机器学习模型,用于对电子商务商品原材料进行分类,提高准确性并减少手动标签. 自主监督学习方法在分类面粉数据方面取得了91%的准确性.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 电子商务 技术 技术 电子商务
背景情况:
- 当前的电子商务项目分类依赖于功能,导致对具有相同或不同的原材料的项目进行错误分类.
- 这种功能分类阻碍了准确的基于原材料的销售统计,并需要广泛的人工标签,用于工程应用.
研究的目的:
- 为电子商务平台开发自动化商品原材料分类系统.
- 解决以功能为导向的分类和手动标签过程的局限性.
主要方法:
- 开发了一个项目概念模型,定义原材料的类别和属性.
- 实施了对物品规格样本和类别标签的自动选.
- 创建了一个特定领域的词典,用于提取原材料特征.
- 利用基于自主监督学习的机器学习分类器进行自动分类.
主要成果:
- 成功选的项目规格样本和自动分配的类别标签.
- 使用特定领域的词汇提取关键原材料特征.
- 在使用拟议的自主监督学习模型对电子商务项目的原材料进行分类时,获得了91%的准确率.
- 使用来自中国电子商务平台的面粉数据验证了该模型.
结论:
- 拟议的项目概念模型和自主监督学习方法有效地自动化了电子商务项目原材料分类.
- 这种方法克服了面向功能分类和手动标签的挑战,提高了销售统计和工程应用的数据准确性.
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