  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.
