Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems

Researchers introduce SCALR, a framework that generates synthetic user-item interaction data across recommendation system domains by leveraging observed events from source domains. The approach addresses data sparsity challenges in large-scale recommendation systems and demonstrates statistically significant improvements in industrial A/B testing.

SCALR represents a methodological advancement in cross-domain recommendation systems by adapting synthetic data generation techniques from the LLM space to solve a persistent infrastructure challenge. The framework addresses a fundamental problem: recommendation systems struggle with sparse user interaction data and noisy signals, particularly when scaling across multiple domains. Rather than relying on traditional knowledge distillation approaches, SCALR translates observed user behavior from source domains into synthetic interactions within target domains, creating augmented training datasets.

The two-stage modular design demonstrates technical sophistication. The first stage estimates conditional probabilities of user-item interactions by decomposing cross-domain transfer as a likelihood estimation problem. The second stage integrates synthetic events into training pipelines in a model-agnostic manner, enabling broader adoption across diverse recommendation architectures. This modularity mirrors successful patterns in machine learning systems where separating concerns improves robustness and generalizability.

For industrial platforms managing recommendation systems at scale, this approach offers practical value. Online A/B testing validation on production systems suggests the framework delivers measurable performance gains beyond theoretical improvements. The model-agnostic integration means existing infrastructure can adopt the technique without architectural redesign, lowering implementation barriers.

The positioning of cross-domain transfer as synthetic data generation opens research pathways exploring quality metrics for synthetic interactions, optimal translation strategies between domains, and potential biases introduced through generation. As recommendation systems become increasingly central to user engagement and revenue, efficiency gains in handling sparse data directly impact platform performance and developer productivity across major technology companies.

• →SCALR generates synthetic user-item interactions across domains to address data sparsity in large-scale recommendation systems.
• →The two-stage framework decomposes cross-domain learning into event translation and model-agnostic training augmentation.
• →Industrial A/B testing demonstrates statistically significant performance improvements over traditional knowledge distillation approaches.
• →Model-agnostic design enables broader adoption without requiring architectural changes to existing recommendation systems.
• →This approach represents early application of synthetic data generation techniques from LLMs to recommendation system infrastructure.