ATLAS: Learning to Recommend Across Unseen Domains

2026-08-04

AI Summary

Traditional recommender systems struggle to generalize to unseen domains. To address this limitation, this work proposes ATLAS, a novel framework that achieves cross-domain zero-shot recommendation without requiring target-domain adaptation or pretraining of large language models. ATLAS learns shared, domain-invariant user–item representations from multiple heterogeneous source domains by integrating Gromov–Wasserstein alignment, adversarial domain confusion, and a residual vector quantization (RVQ) codebook to construct a discrete hierarchical embedding space. Experimental results demonstrate that ATLAS improves the average HitRate by 24% over state-of-the-art baselines across ten unseen Amazon domains, highlighting the critical role of source-domain diversity in enhancing model generalization.

Abstract

Recommender systems remain domain-bound: a model trained on one interaction environment typically requires retraining or target-domain adaptation before it can operate on a new catalogue. A recommender trained on movies cannot be directly deployed to recommend groceries or video games. Existing approaches mitigate this by transferring restricted forms of recommendation knowledge, adapting to the target domain, or leveraging large language models (LLMs) for transferable representations. We instead ask whether recommendation-specific knowledge learned solely from multiple heterogeneous domains can generalize to entirely unseen domains without target-domain adaptation or language-model pretraining. We introduce ATLAS, a multi-source recommendation domain generalization framework that learns a shared, domain-invariant user-item representation from disjoint source domains, enabling zero-shot recommendation on unseen domains. ATLAS combines a Gromov-Wasserstein alignment that preserves how users relate to one another across domains, an adversarial objective that makes item representations indistinguishable across domains, and residual vector quantization (RVQ) codebooks that compress user and item embeddings into a discrete latent space, capturing hierarchical interaction patterns while suppressing domain-specific variation. Trained on five Amazon domains and applied directly to ten unseen domains, ATLAS outperforms state-of-the-art sequential, graph-based, cross-domain, quantization-based, and LLM-based baselines on most unseen domains, with an average relative gain in HitRate of 24%. Ablations and representation analyses validate each component, and we identify a pronounced source-domain diversity effect: increasing source heterogeneity substantially improves zero-shot transfer. ATLAS establishes recommendation domain generalization as a promising paradigm for zero-shot recommendation.

Problem

• cross-domain recommendation
• domain generalization
• zero-shot recommendation
• unseen domains
• recommendation systems

Innovation

• domain generalization
• zero-shot recommendation
• Gromov-Wasserstein alignment
• adversarial learning
• residual vector quantization