Recommender systems are frequently challenged by the data sparsity problem. One approach to mitigate this issue is through cross-domain recommendation techniques. In a cross-domain context, sharing knowledge between domains can enhance the effectiveness in the target domain. Recent cross-domain methods have employed a pre-training approach, but we argue that these methods often result in suboptimal fine-tuning, especially with large neural models. Modern language models utilize prompts for efficient model tuning. Such prompts act as a tunable latent vector, allowing for the freezing of the main model parameters. In our research, we introduce the Personalised Graph Prompt-based Recommendation (PGPRec) framework. This leverages the advantages of prompt-tuning. Within this framework, we formulate personalized graph prompts item-wise, rooted in items that a user has previously engaged with. Specifically, we employ Contrastive Learning (CL) to produce pre-trained embeddings that offer greater generalizability in the pre-training phase, ensuring robust training during the tuning phase. Our evaluation of PGPRec in cross-domain scenarios involves comprehensive testing with the top-k recommendation tasks and a cold-start analysis. Our empirical findings, based on four Amazon Review datasets, reveal that the PGPRec framework can decrease the tuned parameters by as much as 74%, maintaining competitive performance. Remarkably, there's an 11.41% enhancement in performance against the leading baseline in cold-start situations.

翻译：推荐系统常面临数据稀疏问题的挑战。缓解此问题的一种方法是通过跨域推荐技术。在跨域背景下，域间知识共享可提升目标域的有效性。近期跨域方法采用预训练方式，但我们认为这些方法在大规模神经模型中常导致次优微调。现代语言模型利用提示实现高效模型调优，此类提示作为可调谐潜在向量，可冻结主模型参数。在本研究中，我们提出基于个性化图提示的推荐（PGPRec）框架，该框架充分利用了提示调优的优势。在此框架内，我们基于用户历史交互项逐项构建个性化图提示，具体而言，采用对比学习（CL）生成更具泛化性的预训练嵌入，确保在预训练阶段实现强健训练，并在调优阶段保持鲁棒性。我们针对跨域场景下的PGPRec框架进行了全面评估，包括top-k推荐任务测试和冷启动分析。基于四个Amazon Review数据集的实证结果表明，PGPRec框架可将调优参数减少高达74%，同时保持竞争性能。值得注意的是，在冷启动场景下，该框架相较于最佳基线实现了11.41%的性能提升。