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Cross-Domain Recommendation (CDR) stands as a pivotal technology addressingissues of data sparsity and cold start by transferring general knowledge fromthe source to the target domain. However, existing CDR models sufferlimitations in adaptability across various scenarios due to their inherentcomplexity. To tackle this challenge, recent advancements introduce universalCDR models that leverage shared embeddings to capture general knowledge acrossdomains and transfer it through "Multi-task Learning" or "Pre-train, Fine-tune"paradigms. However, these models often overlook the broader structural topologythat spans domains and fail to align training objectives, potentially leadingto negative transfer. To address these issues, we propose a motif-based promptlearning framework, MOP, which introduces motif-based shared embeddings toencapsulate generalized domain knowledge, catering to both intra-domain andinter-domain CDR tasks. Specifically, we devise three typical motifs:butterfly, triangle, and random walk, and encode them through a Motif-basedEncoder to obtain motif-based shared embeddings. Moreover, we train MOP underthe "Pre-training \& Prompt Tuning" paradigm. By unifying pre-training andrecommendation tasks as a common motif-based similarity learning task andintegrating adaptable prompt parameters to guide the model in downstreamrecommendation tasks, MOP excels in transferring domain knowledge effectively.Experimental results on four distinct CDR tasks demonstrate the effectivenessof MOP than the state-of-the-art models.

Motif-Based Prompt Learning for Universal Cross-Domain Recommendation