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.

翻译：在线卖家和广告主会为其上架商品获得推荐关键词，并通过竞价这些关键词来提升销量。生成此类推荐的一种主流范式是极端多标签分类（XMC），其核心是将关键词标注/映射至商品。本文阐述了在电子商务平台上使用传统的基于商品-查询的标注或映射技术进行关键词推荐所存在的局限性。我们提出了GraphEx，这是一种创新的基于图的方法，通过从商品标题中提取词元排列来为卖家推荐关键词。此外，我们论证了在实际应用中仅依赖精确率/召回率等传统评估指标可能产生误导，因此需要结合多种指标来评估真实场景下的性能。这些指标旨在评估关键词与商品的相关性及其触达买家的潜力。GraphEx在eBay的生产模型中表现优异，成功实现了上述目标。该方法能够在资源受限的生产环境中支持近实时推理，并可有效扩展至数十亿级别的商品规模。