Dual Conditional Diffusion for Generative Cross-Domain Recommendation via Disentangled Knowledge Transfer

Abstract

Cross-domain recommendation (CDR) mitigates data sparsity by transferring knowledge across domains, and dual-target CDR further enables bidirectional transfer to improve both domains simultaneously. However, existing methods largely rely on linear augmentation or reconstruction, which may generate semantically inconsistent representations and noisy supervision. To address these limitations, we propose DCD-XRec, a Dual Conditional Diffusion framework that models cross-domain knowledge transfer as a structured and non-linear generative process. The model first learns expressive user embeddings with graph-based encoders and then decomposes them into shared and domain-specific components using learnable projection heads. On this basis, a bidirectional conditional diffusion module synthesizes target-domain embeddings conditioned on source-domain representations, enabling semantically coherent transfer. In addition, we introduce a dual diffusion objective with two complementary pathways: a real-to-real pathway for stable alignment and a real-to-augmented pathway for richer generative supervision. Experiments on multiple public benchmark datasets show that DCD-XRec consistently outperforms strong baselines, demonstrating the effectiveness of diffusion-based generative modeling for dual-target cross-domain recommendation.