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

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. Advertisers or sellers on e-commerce platforms are recommended keyphrases by the platform to increase their item visibility depending on buyer searches as shown in Figure 1a. The sellers bid on relevant recommended keyphrases as well as their own custom keyphrases to increase their sales (Figure 1b). Keyphrase recommendation is the subject of a lot of research and well studied in several works [1], [2], [3], [4], [5], [6], [7], due to their challenging research and industrial importance. Additionally, the platform matches the (recommended) keyphrases bid by the sellers to the buyer search queries [8].