Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target domain. However, existing CDR models suffer limitations in adaptability across various scenarios due to their inherent complexity. To tackle this challenge, recent advancements introduce universal CDR models that leverage shared embeddings to capture general knowledge across domains and transfer it through "Multi-task Learning" or "Pre-train, Fine-tune" paradigms. However, these models often overlook the broader structural topology that spans domains and fail to align training objectives, potentially leading to negative transfer. To address these issues, we propose a motif-based prompt learning framework, MOP, which introduces motif-based shared embeddings to encapsulate generalized domain knowledge, catering to both intra-domain and inter-domain CDR tasks. Specifically, we devise three typical motifs: butterfly, triangle, and random walk, and encode them through a Motif-based Encoder to obtain motif-based shared embeddings. Moreover, we train MOP under the "Pre-training \& Prompt Tuning" paradigm. By unifying pre-training and recommendation tasks as a common motif-based similarity learning task and integrating adaptable prompt parameters to guide the model in downstream recommendation tasks, MOP excels in transferring domain knowledge effectively. Experimental results on four distinct CDR tasks demonstrate the effectiveness of MOP than the state-of-the-art models.

翻译：跨域推荐（CDR）是一项关键技术，通过将源域中的通用知识迁移到目标域，来解决数据稀疏性和冷启动问题。然而，现有CDR模型因其固有复杂性，在不同场景下的适应性存在局限。为应对这一挑战，近期研究引入了通用CDR模型，通过共享嵌入捕获跨域通用知识，并采用"多任务学习"或"预训练-微调"范式进行知识迁移。然而，这类模型常忽视跨域的整体结构拓扑，且未能对齐训练目标，可能导致负迁移。为解决这些问题，我们提出基于模体的提示学习框架MOP，该框架引入基于模体的共享嵌入以封装泛化的域知识，适用于域内与域间CDR任务。具体地，我们设计了三种典型模体：蝴蝶模体、三角形模体和随机游走模体，并通过基于模体的编码器对其进行编码，获取基于模体的共享嵌入。此外，我们采用"预训练与提示调优"范式训练MOP。通过将预训练任务与推荐任务统一为基于模体的相似性学习任务，并集成可调提示参数以引导模型在推荐下游任务中的行为，MOP在域知识迁移方面表现出色。在四个不同CDR任务上的实验结果表明，MOP的有效性优于当前最先进的模型。