How Yahoo enhances search retargeting using Amazon Bedrock

<p>Connecting user search intent with relevant ad experiences across channels is a longstanding challenge in digital advertising. Advertisers need sophisticated ways to reach audiences based on their demonstrated interests and behaviors, particularly their search activity, which is one of the strongest signals of user intent. Traditional keyword expansion approaches often struggle with outdated vocabulary, limited semantic understanding, and inability to capture the nuanced meaning behind search queries.</p> <p>The <a href="https://www.yahooinc.com/" target="_blank" rel="noopener">Yahoo</a> omnichannel Demand-Side Platform (DSP) addresses these challenges by providing advertisers with advanced technology, premium supply access, vast scale, and trusted consumer relationships. A DSP lets advertisers purchase ad inventory across multiple exchanges and channels through a single interface. Yahoo DSP enhances this capability with sophisticated audience targeting powered by advertiser-defined rules, machine learning (ML), and generative AI.</p> <p>At the heart of this targeting is the concept of an audience segment: a group of users who share specific interests, demographics, or behaviors and can be targeted collectively in advertising campaigns. These segments are passed to targeting systems for ad serving, making sure that users are matched with the most relevant advertiser campaigns.</p> <p>In this post, we demonstrate how Yahoo implemented <a href="https://aws.amazon.com/bedrock/" target="_blank" rel="noopener">Amazon Bedrock</a> to enhance their Search Retargeting (SRT) capabilities in the Yahoo DSP ad tech suite. SRT is a core audience targeting solution that helps advertisers reach users based on their historical search behavior, bridging search intent with display, video, and native advertising. Beyond targeting keywords entered on Yahoo Search, SRT uses AI to identify and engage users who demonstrate intent through search activity both on Yahoo and across integrated partner systems.</p> <h2>Business challenges</h2> <p>Previously, the Yahoo SRT architecture used the Word2Vec embedding model combined with locality-sensitive hashing (LSH) to expand advertiser keywords. This multi-step process involved canonicalizing keywords, generating embeddings, indexing them, performing LSH searches, filtering sensitive terms, and ranking the results.</p> <p>However, the Word2Vec + LSH approach had several limitations:</p> <ul> <li><strong>Outdated vocabulary</strong>: It didn’t always reflect current terms.</li> <li><strong>Phrase-based searches</strong>: It focused on phrases rather than specific entities.</li> <li><strong>Syntactic similarity</strong>: Expansion relied on syntactic rather than semantic meaning.</li> <li><strong>Zero expansion</strong>: In some cases, it failed to generate any new keywords.</li> </ul> <p>We addressed these challenges by deploying generative AI–powered SRT keyword expansion using Amazon Bedrock and large language models (LLMs). This enhancement generates more relevant, semantically rich keyword expansions, helping advertisers reach larger and more precise audiences.</p> <h2>Solution overview</h2> <p>Before introducing the solution, it’s useful to review the legacy SRT workflow, illustrated in the following diagram. Advertisers begin by defining each segment through a set of target keywords. This metadata is stored in a SQL database. The backend system then performs keyword expansion, generating semantically related terms to increase audience reach and improve advertiser outcomes. Both the original and expanded keyword sets are persisted in an <a href="https://aws.amazon.com/opensearch-service/" target="_blank" rel="noopener">Amazon OpenSearch Service</a> cluster. Another workflow called Batch Scoring evaluates Yahoo users’ search histories against this segment metadata to determine segment membership.</p> <p><img src="https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b1