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BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations

  • Ashirbad Mishra
  • , Jinyu Zhao
  • , Soumik Dey
  • , Hansi Wu
  • , Binbin Li
  • , Kamesh Madduri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2025 IEEE International Conference on Big Data, BigData 2025
EditorsCheng-Zhong Xu, Leong Hou U, Xueqi Cheng, Jing Gao, Giuseppe Polese, Hong Mei, Paul Boniol, Michiaki Tatsubori, Chen Zhao, Dawei Zhou, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2415-2424
Number of pages10
Edition2025
ISBN (Electronic)9798331594473
DOIs
StatePublished - 2025
Event2025 IEEE International Conference on Big Data, BigData 2025 - Macau, China
Duration: Dec 8 2025 → Dec 11 2025

Conference

Conference2025 IEEE International Conference on Big Data, BigData 2025
Country/TerritoryChina
CityMacau
Period12/8/25 → 12/11/25

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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