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

2026-05-29 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The Synthetic Cross-domain Augmentation and Learning for Recommendation (SCALR) framework addresses data sparsity and noisy implicit feedback in large-scale, cross-domain recommendation systems. Inspired by synthetic data generation in LLMs, SCALR generates synthetic user-item interaction events for a target recommendation domain by leveraging observed events from a source domain. The framework operates in two modular stages: first, it translates observed user events in source domains by estimating the likelihood of a user interacting with a target-domain item, conditioned on their source interactions. Second, downstream models train on these synthetic events as cross-domain learning objectives, augmenting the target domain's training data in a model-agnostic manner. This approach yielded statistically significant improvements in online A/B tests on an industrial recommendation platform, marking it as an early work framing cross-domain event transfer as synthetic data generation.

Key takeaway

For Machine Learning Engineers building large-scale recommendation systems and encountering data sparsity or noisy implicit feedback across domains, consider adopting synthetic data generation. SCALR demonstrates that framing cross-domain event transfer as synthetic data generation can significantly augment target domain training data, leading to measurable improvements. You should explore decomposing cross-domain learning into event translation and model-agnostic data augmentation to enhance system performance.

Key insights

SCALR generates synthetic user-item interaction events from source domains to augment target domain data for recommendation systems.

Principles

• Decompose cross-domain learning into modular stages.
• Frame event generation as likelihood estimation.
• Augment target data with model-agnostic synthetic events.

Method

SCALR translates source domain user events by estimating target-domain item interaction likelihood, then uses these synthetic events as cross-domain learning objectives to augment target domain training data.

In practice

• Generate synthetic user-item interactions.
• Improve recommendation systems via data augmentation.
• Apply cross-domain event transfer.

Topics

• Recommendation Systems
• Synthetic Data Generation
• Cross-Domain Learning
• Data Augmentation
• Information Retrieval
• Machine Learning

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.