Domain Adaptive Learning for Ads

1 post · May 2026

What this trail covers

Instacart is enhancing its advertising platform (Carrot Ads) by developing and applying Domain Adaptive Learning techniques. This approach transfers knowledge from the data-rich Instacart Marketplace (source domain) to new partner websites (target domains) to address the 'cold start' problem. This enables performant ad serving even with limited historical interaction data on partner sites. The method involves adapting both the neural network architecture (reusing and fine-tuning embedding layers) and the training data (matching features across domains). The goal is to onboard new retail partners faster and more scalably, delivering performant ad systems from day one.

All Instacart trails

May 2026Empowering Carrot Ads with Domain Adaptive LearningThis post details Instacart's implementation of Domain Adaptive Learning for its Carrot Ads platform. It addresses the 'cold start' problem for new retail partners by transferring knowledge from the Instacart Marketplace. The approach involves adapting a wide and deep pCTR model architecture at both the Neural Network level (reusing and fine-tuning shared embedding layers, feature transfer, and fine-tuning specific layers) and the Training Data level (matching features between source and target domains, and feature trimming for latency). The post highlights benefits like improved performance in low-data scenarios, efficiency, and generalization, while also noting the current need for human-in-the-loop verification and the future development of an automated Domain Adaptation Platform.
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