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

• URL: http://arxiv.org/abs/2606.00282v1
• Date: Fri, 29 May 2026 19:17:50 GMT
• Title: Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems
• Abstract summary: We introduce Synthetic Cross-domain Augmentation and Learning for Recommendation (SCALR)<n>SCALR generates synthetic user-item interaction events for a target recommendation domain by leveraging observed events from a source domain.<n>Our approach yields statistically significant improvements in online A/B tests on an industrial recommendation platform.
• Score: 8.895562782206257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback. Traditional approaches mitigate this via model-specific knowledge distillation from source domains to a target domain. Inspired by the transformative success of synthetic data generation in large language models (LLMs), we introduce Synthetic Cross-domain Augmentation and Learning for Recommendation (SCALR), a framework that generates synthetic user-item interaction events for a target recommendation domain by leveraging observed events from a source domain. SCALR decomposes cross-domain learning into two modular stages. First, it translates observed user events in source domains by framing event generation as estimating the likelihood that a user would interact with a target-domain item, conditioned on their observed interactions in a source domain. Second, downstream models train on these synthetic events as cross-domain learning objectives, where the synthetic events augment the target domain's training data in a model-agnostic manner. Our approach yields statistically significant improvements in online A/B tests on an industrial recommendation platform. To the best of our knowledge, this is among the first works to explicitly frame cross-domain event transfer as synthetic data generation for recommendation systems.

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