用商品标题生成关键词推荐，提升电商广告效果
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation
- 基于商品标题的词元排列提取，构建图结构推荐关键词
- 在亿级商品上实现近实时推理，性能优于现有生产模型
- 结合多指标评估，更贴近实际买家触达需求
在线卖家和广告商被推荐关键词用于商品投放以提升销量。主流方法为极端多标签分类（XMC），即对商品打标签或映射关键词。本文指出传统基于商品-查询的标签技术在电商平台关键词推荐中存在局限。提出GraphEx，一种基于图的关键词推荐方法，通过从商品标题中提取词元排列来生成推荐。同时强调仅依赖精度/召回率等传统指标可能误导实际应用，需结合评估关键词相关性与潜在买家触达能力的多维度指标。GraphEx在eBay生产环境中表现超越现有模型，支持资源受限环境下的近实时推理，并可有效扩展至十亿级商品规模。
原文摘要 · Abstract (English)
Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales. One popular paradigm that generates such recommendations is Extreme Multi-Label Classification (XMC), which involves tagging/mapping keyphrases to items. We outline the limitations of using traditional item-query based tagging or mapping techniques for keyphrase recommendations on E-Commerce platforms. We introduce GraphEx, an innovative graph-based approach that recommends keyphrases to sellers using extraction of token permutations from item titles. Additionally, we demonstrate that relying on traditional metrics such as precision/recall can be misleading in practical applications, thereby necessitating a combination of metrics to evaluate performance in real-world scenarios. These metrics are designed to assess the relevance of keyphrases to items and the potential for buyer outreach. GraphEx outperforms production models at eBay, achieving the objectives mentioned above. It supports near real-time inferencing in resource-constrained production environments and scales effectively for billions of items.
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