# Empowering Carrot Ads with Domain Adaptive Learning

DevFeed: [Empowering Carrot Ads with Domain Adaptive Learning](<https://devfeed.tech/articles/empowering-carrot-ads-with-domain-adaptive-learning-20104.md>)

Original publisher: [Read original article](<https://tech.instacart.com/empowering-carrot-ads-with-domain-adaptive-learning-870730e6add5?source=rss----587883b5d2ee---4>)

Author: Xiyu Wang

Published: 2026-05-04T19:11:17Z

Content type: article

Language: en

Sources: [Instacart](<https://devfeed.tech/sources/instacart.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [data](<https://devfeed.tech/topics/data.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Website](<https://devfeed.tech/topics/website.md>)

Tags: [advertising](<https://devfeed.tech/tags/advertising.md>), [ai](<https://devfeed.tech/tags/ai.md>), [data](<https://devfeed.tech/tags/data.md>), [domain](<https://devfeed.tech/tags/domain.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [retail](<https://devfeed.tech/tags/retail.md>)

## AI overview

The article explains how Instacart applies Domain Adaptive Learning to Carrot Ads to address the cold-start problem for new retail partner websites. It describes transferring knowledge from Instacart Marketplace data to target domains to improve click-through-rate prediction and ad ranking with limited partner-specific interaction data.

## Source excerpt

Authors: Trey Zhong, Xiyu Wang Contributors: Joseph Haraldson, Sharad Gupta, Sarah Lamacchia Introduction Carrot Ads is Instacart's omnichannel retail media solution that allows retailer partners to build and scale their own advertising businesses on either their owned-and-operated (O&O) websites and apps or their whitelabel Storefront hosted by Instacart. Carrot Ads empowers retailers and CPG brands to accelerate revenue, while improving the customer experience, engagement and Ads return on investment. It features enterprise-grade infrastructure, AI-powered optimization, years of proprietary first-party data and flexibility to choose from retailer-sourced Ads demand, Instacart-sourced demand from 7,500+ CPG brands, or both. However, onboarding a new partner onto Carrot Ads introduces a key challenge: the 'cold start' problem, where limited historical interactions make it difficult to predict user behavior accurately. To serve performant ads, our systems rely on predicting a user's Click-Through Rate (CTR) to generate a ranking score. On the Instacart Marketplace, we have billions of historical signals to train a model to do so. But when a partner launches a new ads experience on their O&O e-commerce site, there is often little to no interaction history for that property, so training an accurate model becomes challenging. User behavior can vary dramatically between websites -- for example, browsing patterns on a grocery site differ from those on a pet supply or electronics site. Training a model from scratch for a new domain is data hungry. Conversely, directly deploying Instacart's existing Marketplace model often fails to capture the nuances of the partner's specific inventory and user base. To address this, we developed a Domain Adaptive Learning approach that transfers knowledge from Instacart's data-rich environment to new partner environments. By treating the Instacart Marketplace as a source domain and the partner's website as a target domain, we can transfer