GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation

TL;DR

GraphEx is a novel graph-based method for extracting and recommending keyphrases for e-commerce products, outperforming existing models and suitable for large-scale, real-time applications.

Contribution

We introduce GraphEx, a new graph-based keyphrase extraction approach that addresses limitations of traditional methods and improves performance in large-scale e-commerce settings.

Findings

GraphEx outperforms existing production models at eBay.

It supports near real-time inference in resource-constrained environments.

The evaluation highlights the importance of combined metrics over traditional precision/recall.

Abstract

Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales. One popular paradigm that generates such recommendations is Extreme Multi-Label Classification (XMC), which involves tagging/mapping keyphrases to items. We outline the limitations of using traditional item-query based tagging or mapping techniques for keyphrase recommendations on E-Commerce platforms. We introduce GraphEx, an innovative graph-based approach that recommends keyphrases to sellers using extraction of token permutations from item titles. Additionally, we demonstrate that relying on traditional metrics such as precision/recall can be misleading in practical applications, thereby necessitating a combination of metrics to evaluate performance in real-world scenarios. These metrics are designed to assess the relevance of keyphrases to items and the potential for buyer outreach. GraphEx outperforms production models at eBay, achieving the objectives mentioned above. It supports near real-time inferencing in resource-constrained production environments and scales effectively for billions of items.