--- title: Empowering Carrot Ads with Domain Adaptive Learning source: Instacart Engineering source_slug: instacart url: https://tech.instacart.com/empowering-carrot-ads-with-domain-adaptive-learning-870730e6add5?source=rss----587883b5d2ee---4 published: 2026-05-04 fetched: 2026-05-05T14:00:55+00:00 ingested: true --- # Empowering Carrot Ads with Domain Adaptive Learning Press enter or click to view image in full size 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. Press enter or click to view image in full size 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 knowledge to bootstrap performance with a relatively smaller amount of data. We also found that even when there is enough data to train a model directly on the target domain, the domain adaptive model still performs better because of the benefits from Instacart’s first party data. ## Domain Adaptive Learning ## What is Domain Adaptive Learning? At a high level, Domain Adaptive Learning is a subset of **transfer learning**. It focuses on transferring knowledge gained from solving a problem in a data-rich environment (source domain) to improve performance in a related, often data-scarce environment (target domain). ## Get Xiyu Wang’s stories in your inbox Join Medium for free to get updates from this writer. Remember me for faster sign in Instead of initializing a new model with random weights for every partner, we reuse representations and relationship signals learned from Instacart marketplace data to “warm start” the model. This saves labeled data and computational power, but more importantly, it allows us to deploy performant models in scenarios where the target domain lacks sufficient history to converge on its own. ## Benefits & Challenges The benefits of domain adaptive learning are significant. * **Performance: \- In Low-Data Scenarios**: Allows models to perform well, even with limited labeled data in the target domain. \- **In High-Data Scenarios:** Improves performance beyond what models trained solely on the target domain can achieve. * **Efficiency:** Drastically reduces training time and development costs for new domains by reusing pre-trained components. * **Generalization** : Enables robust models that can generalize well in different but related domains, even when there are distribution shifts. ## Model Architecture The Domain Adaptive Learning method is based on a wide and deep Predicted Click-Through-Rate (pCTR) model architecture commonly used in large-scale recommendation systems. This model predicts CTR by first transforming raw inputs, like user IDs and product text, into dense feature embeddings. These features are concatenated and processed through two parallel paths: an interaction layer for learning explicit feature interactions and a deep Multi-layer Perceptron (MLP) tower for learning complex, hidden patterns. The outputs are then merged and passed through a final MLP to synthesize the findings. Finally, a Sigmoid activation squashes the result into a probability score (pCTR) between 0 and 1. This architecture combines a linear “wide” model (for memorization of specific feature interactions) with a “deep” neural network (for generalization). More details about this architecture can be found at this other [blog post](/one-model-to-serve-them-all-0eb6bf60b00d). Press enter or click to view image in full size Our strategy for domain adaptation occurs at two distinct layers: the **Neural Network** level and the **Training Data** level. ## Domain Adaptation At Neural Network Level In Instacart Ads’ pCTR model, transfer learning at the neural network level involves reusing and fine-tuning components from a pre-trained model that originated from a related domain or task. Specifically: 1. **Shared Embedding Layers** : The model utilizes embedding representation layers pre-trained on shopping contexts. These embeddings capture fundamental signals that are transferable. 2. **Feature Transfer Domain Adaption** : The model structure allows seamless integration of pre-trained embeddings with domain-specific input features. For instance, “Wide” components might focus on explicit features (e.g. historical CTR for a product category) sampled from the new domain, while “Deep” components adapt pre-trained dense representations. 3. **Fine-Tuning Specific Layers** : While shared layers are reused without major alterations, subsequent layers are fine-tuned using limited partner-specific training data to capture domain-specific behavior. 4. **Generalization** : Transfer learning ensures the model can generalize knowledge learned from user interactions in Instacart Marketplace to predict user responses in the partner’s domain. This prevents the need to train the deep ranker entirely from scratch. ## Domain Adaptation At Training Data Level Transfer learning at the data level involves aligning the input signals of the source and target domains so the model “speaks the same language.” We rely on aggregated historical performance signals to normalize features across domains, but there are a variety of details that contribute to the quality of our training data. * **Source Data** : Large-scale data from Instacart Marketplace’s user behavior is leveraged as the source domain. This data is used to pre-train embeddings and build a foundational model. * **Matching Features Between Domains** : Common contextual and catalog-level features between the Instacart Marketplace’s catalog data and the Carrot Ads Partner’s catalog are aligned (e.g. ensuring product category uses the same taxonomy) to ensure the source domain knowledge is transferable. * **Feature Trimming for Latency Optimization** : To meet real-time auction latency requirements and be flexible to various feature availability for the partners, we apply feature trimming technique to balance performance and speed. We analyze feature importance in the target domain and prune inputs that do not contribute to prediction accuracy for that specific partner, ensuring the model remains lightweight. Press enter or click to view image in full size ## **Learnings & Conclusions** Our evaluation of Domain Adaptive Learning demonstrates that it is possible to achieve satisfactory pCTR prediction accuracy with limited data from our partners. By leveraging the “source” knowledge of the Instacart Marketplace, we achieved higher CTR, total clicks per user and ads revenue across search ads and product category ads. This approach enables us to launch high-performing ad networks for partners immediately, eliminate the traditional data ramp-up period and converge to a better stable state. However, this process is not yet fully autonomous. The complexity of mapping data schemas and verifying model alignment currently requires human-in-the-loop verification to prevent negative transfer. Looking ahead, we are building an automated **Domain Adaptation Platform** that can detect domain shifts and fundamentally streamline the workflow. This allows us to onboard new retail partners faster and in a more scalable way, while continuing to deliver performant ad systems from day one. ## References