MCRPL: A Pretrain, Prompt, and Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation.

The article introduces MCRPL, a paradigm combining pretraining, prompt, and fine-tuning, to address the challenge of non-overlapping many-to-one cross-domain recommendation. Topics include the integration of domain-agnostic and domain-dependent prompts, updating prompts for knowledge transfer, and experimental validation against recent baselines, demonstrating superior performance.