GraphEx：一种基于图的广告主关键词推荐提取方法
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation
AI总结：
提出GraphEx，一种基于图的关键词推荐方法，通过从商品标题提取词元排列生成推荐，并改进评估指标，在eBay上优于生产模型，支持近实时推理和数十亿商品规模。
摘要：
在线卖家和广告主会为其列出的商品获得推荐关键词，并对其进行竞价以提升销售额。生成此类推荐的一种流行范式是极端多标签分类（XMC），它涉及将关键词标记/映射到商品。我们概述了在电子商务平台上使用传统的基于商品-查询的标记或映射技术进行关键词推荐的局限性。我们引入了GraphEx，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向卖家推荐关键词。此外，我们证明，在实际应用中，依赖精确率/召回率等传统指标可能会产生误导，因此需要结合多种指标来评估现实场景中的性能。这些指标旨在评估关键词与商品的相关性以及吸引买家的潜力。GraphEx在eBay上优于生产模型，实现了上述目标。它支持在资源受限的生产环境中进行近实时推理，并能有效扩展到数十亿商品。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
