BroadGen：一种生成有效且高效的广告商广义匹配关键词推荐框架
BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations
AI总结：
本文提出BroadGen框架，通过历史搜索数据生成高效有效的广义匹配关键词，提升查询稳定性，支持eBay数千万卖家。
摘要：
在赞助搜索广告领域，关键词推荐的重点大多集中在精确匹配类型上，这导致了高管理成本、有限的目标范围和不断变化的搜索查询模式等问题。替代方案如广义匹配类型可以缓解精确匹配的一些缺点，但存在目标准确性差和由于广告商使用有限而监督信号少的挑战。本研究定义了理想广义匹配的标准，强调效率和有效性，确保大部分匹配查询的相关性。我们提出了BroadGen，一种创新的框架，通过利用历史搜索查询数据来推荐高效且有效的广义匹配关键词。此外，我们证明BroadGen通过词元对应建模，在时间上保持更好的查询稳定性。BroadGen的能力使其能够为eBay上的数千万卖家提供服务，涵盖超过250亿件商品。
英文摘要：
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.
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。
