提出BroadGen框架，帮广告主高效精准推荐广泛匹配关键词。
BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations
- 基于历史搜索数据建模，智能生成高效且精准的广泛匹配关键词。
- 在超过25亿商品上验证，显著提升查询匹配相关性与稳定性。
- 适合电商广告平台大规模部署，降低广告管理成本。
在赞助式搜索广告领域，关键词推荐长期聚焦于精确匹配，存在管理成本高、覆盖范围有限及搜索模式变化快等问题。广泛匹配虽可缓解部分缺陷，却面临定位不准、监督信号弱等挑战。本文定义了理想广泛匹配的标准：兼顾效率与效果，确保多数匹配查询具有相关性。提出BroadGen框架，利用历史搜索查询数据生成高效且精准的广泛匹配关键词。此外，通过分词对应建模，BroadGen能有效保持查询随时间的稳定性。该能力已支持eBay每日服务数百万卖家，覆盖超25亿商品。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。