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This paper introduces Language-Guided Conditional Diffusion for Cross-Domain Recommendation (LGCD), a framework that leverages LLMs to infer target domain preferences for single-domain users, creating pseudo-overlapping data to address data sparsity in inter-domain recommendation. LGCD uses LLM reasoning to bridge the domain gap and a conditional diffusion architecture to generate target user representations based on source-domain patterns. Experiments show LGCD outperforms state-of-the-art methods, demonstrating the effectiveness of LLM-guided diffusion models for preference transfer in sparse cross-domain scenarios.
LLMs can hallucinate enough signal to make cross-domain recommendations work even when users have no overlapping history.
Cross-domain Recommendation (CDR) exploits multi-domain correlations to alleviate data sparsity. As a core task within this field, inter-domain recommendation focuses on predicting preferences for users who interact in a source domain but lack behavioral records in a target domain. Existing approaches predominantly rely on overlapping users as anchors for knowledge transfer. In real-world scenarios, overlapping users are often scarce, leaving the vast majority of users with only single-domain interactions. For these users, the absence of explicit alignment signals makes fine-grained preference transfer intrinsically difficult. To address this challenge, this paper proposes Language-Guided Conditional Diffusion for CDR (LGCD), a novel framework that integrates Large Language Models (LLMs) and diffusion models for inter-domain sequential recommendation. Specifically, we leverage LLM reasoning to bridge the domain gap by inferring potential target preferences for single-domain users and mapping them to real items, thereby constructing pseudo-overlapping data. We distinguish between real and pseudo-interaction pathways and introduce additional supervision constraints to mitigate the semantic noise brought by pseudo-interaction. Furthermore, we design a conditional diffusion architecture to precisely guide the generation of target user representations based on source-domain patterns. Extensive experiments demonstrate that LGCD significantly outperforms state-of-the-art methods in inter-domain recommendation tasks.
