  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.
