统一的人工智能方法使用编码和生成大型语言模型用于电子商务中的变量产品匹配
Pedro Herrero-Vidal1, You-Lin Chen1, Cris Liu1
1Amazon.com Inc, Seattle, Washington, USA.
Big data
|February 28, 2026
概括
我们开发了一个变异关系匹配策略 (VARM) 来识别电子商务中的类似产品. 这种方法准确地将不同的产品及其不同属性联系起来,改善产品发现和数据组织.
科学领域:
- 人工智能的人工智能
- 电子商务 技术 技术 电子商务
- 数据科学数据科学数据科学
背景情况:
- 传统的实体解决方案侧重于相同的产品匹配,忽视了电子商务中的关键变量关系.
- 识别类似的,不相同的产品对于诸如共享评论和网页列表等应用程序至关重要.
研究的目的:
- 为变种产品关系引入新的实体解决策略.
- 在电子商务目录中准确识别变体产品对及其区分属性.
主要方法:
- 开发了变体关系匹配器 (VARM) 策略.
- 构建了网页产品链接的数据集,以训练编码大型语言模型 (LLM).
- 雇佣的检索-增强生成-促使生成的LLM提取产品的变化和共同点.
主要成果:
- VARM成功地识别了不同的产品匹配及其不同的属性.
- 该战略在现实世界电子商务数据中表现优于其他解决方案.
- 证明了结合编码和生成人工智能的有效性.
结论:
- 通过捕获变种产品链接,VARM建立了电子商务实体解决方案的新标准.
- 这种方法可以更好地利用复杂的产品关系.
- 这些发现对电子商务数据管理和用户体验有重大影响.
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