In the domain of sponsored search advertising, the focus of Keyphrase recommendation has largely been on exact match types, which pose issues such as high management expenses, limited targeting scope, and evolving search query patterns. Alternatives like Broad match types can alleviate certain drawbacks of exact matches but present challenges like poor targeting accuracy and minimal supervisory signals owing to limited advertiser usage. This research defines the criteria for an ideal broad match, emphasizing on both efficiency and effectiveness, ensuring that a significant portion of matched queries are relevant. We propose BroadGen, an innovative framework that recommends efficient and effective broad match keyphrases by utilizing historical search query data. Additionally, we demonstrate that BroadGen, through token correspondence modeling, maintains better query stability over time. BroadGen's capabilities allow it to serve daily, millions of sellers at eBay with over 2.5 billion items.

翻译：在赞助搜索广告领域，关键词推荐的研究主要集中在精确匹配类型上，这带来了管理成本高、定向范围有限以及搜索查询模式不断演变等问题。诸如广泛匹配类型等替代方案可以缓解精确匹配的某些缺点，但由于广告商使用有限，存在定向准确性差和监管信号微弱等挑战。本研究定义了理想广泛匹配的标准，强调效率和效果并重，确保匹配查询中的大部分具有相关性。我们提出了BroadGen，这是一个创新的框架，通过利用历史搜索查询数据来推荐高效且有效的广泛匹配关键词。此外，我们证明BroadGen通过令牌对应建模，能够随时间保持更好的查询稳定性。BroadGen的能力使其能够每日为数以百万计的eBay卖家提供服务，涉及超过25亿件商品。