回报器:基于图形变压器的模型,用于预测电子商务中的产品回报
Qian Cao1, Ning Zhang1, Huiyong Li2
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
零售商现在可以使用 Returnformer,一种基于图形的新型模型,在付款之前预测产品退货. 这有助于减少高回报率和冲动购买造成的损失.
科学领域:
- 电子商务分析分析
- 机器学习 机器学习
- 图形理论就是图形理论.
背景情况:
- 高产品回报率增加了电子商务零售商的成本.
- 宽松的回报政策和冲动性购买有助于提高回报率.
- 在支付之前准确预测回报,对于主动管理是必要的.
研究的目的:
- 开发一种新的模型,用于预测电子商务产品在付款前的退货行为.
- 通过积极的退货管理,减少零售商的潜在财务损失.
主要方法:
- 提议的返回变压器,基于图形变压器的新型返回预测模型.
- 集成的全球拓嵌入,以减轻结构信息丢失.
- 使用的图形转换器用于在子图中捕获远程用户项目依赖.
- 引入了用于全球回报模式传播的图形级注意力机制.
主要成果:
- 与四个机器学习模型相比,Returnformer表现出更高的性能.
- 在现实世界电子商务数据集上实现了更高的预测准确度.
- 在回报预测方面表现优于现有的最先进模型.
结论:
- 提出的Returnformer模型可以在付款前准确预测产品退货风险.
- 零售商可以及时和积极地实施预防性干预,以减少损失.
- 该模型支持电子商务中的主动退货管理.
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