Improving Ad matching via Cluster-Adaptive Keyword Expansion and Relevance tuning

TL;DR

This paper presents a novel semantic keyword expansion method for search advertising that improves ad relevance and CTR by using a siamese model for semantic similarity and a cluster-based thresholding mechanism.

Contribution

It introduces a document-side semantic expansion technique combined with a cluster-aware thresholding and relevance tuning to enhance ad matching accuracy.

Findings

Improved ad relevance and CTR in search advertising.

Scalable and low-latency expansion method.

Effective adaptation of relevance model to expanded keywords.

Abstract

In search advertising, keyword matching connects user queries with relevant ads. While token-based matching increases ad coverage, it can reduce relevance due to overly permissive semantic expansion. This work extends keyword reach through document-side semantic keyword expansion, using a language model to broaden token-level matching without altering queries. We propose a solution using a pre-trained siamese model to generate dense vector representations of ad keywords and identify semantically related variants through nearest neighbor search. To maintain precision, we introduce a cluster-based thresholding mechanism that adjusts similarity cutoffs based on local semantic density. Each expanded keyword maps to a group of seller-listed items, which may only partially align with the original intent. To ensure relevance, we enhance the downstream relevance model by adapting it to the expanded keyword space using an incremental learning strategy with a lightweight decision tree ensemble. This system improves both relevance and click-through rate (CTR), offering a scalable, low-latency solution adaptable to evolving query behavior and advertising inventory.