BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations
This work addresses the challenge of improving broad match keyphrase recommendations for advertisers in sponsored search, offering a domain-specific solution that is incremental in nature.
The paper tackles the problem of poor targeting accuracy and limited supervisory signals in broad match keyphrase recommendations for sponsored search advertising by proposing BroadGen, a framework that uses historical search query data to recommend efficient and effective keyphrases, achieving deployment at eBay to serve millions of sellers daily with over 2.5 billion items.
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.