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.

翻译：跨域推荐利用多领域间的关联性来缓解数据稀疏问题。作为该领域的核心研究方向，域间推荐旨在预测在源域中有交互行为但目标域缺乏行为记录的用户偏好。现有方法主要依赖重叠用户作为知识迁移的锚点。然而在实际场景中，重叠用户往往稀缺，使得绝大多数用户仅具有单域交互行为。对于这些用户，显式对齐信号的缺失使得细粒度的偏好迁移本质上变得困难。针对这一挑战，本文提出语言引导的条件扩散跨域推荐框架，该框架融合大语言模型与扩散模型以实现域间序列推荐。具体而言，我们利用大语言模型的推理能力来弥合域间差距，通过推断单域用户潜在的目标偏好并将其映射到真实物品，从而构建伪重叠数据。我们区分真实交互与伪交互路径，并引入额外的监督约束以缓解伪交互带来的语义噪声。此外，我们设计了条件扩散架构，基于源域模式精确指导目标用户表征的生成。大量实验表明，在域间推荐任务中本框架显著优于现有最先进方法。