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GNN-based recommenders have excelled in modeling intricate user-iteminteractions through multi-hop message passing. However, existing methods oftenoverlook the dynamic nature of evolving user-item interactions, which impedesthe adaption to changing user preferences and distribution shifts in newlyarriving data. Thus, their scalability and performances in real-world dynamicenvironments are limited. In this study, we propose GraphPro, a framework thatincorporates parameter-efficient and dynamic graph pre-training with promptlearning. This novel combination empowers GNNs to effectively capture bothlong-term user preferences and short-term behavior dynamics, enabling thedelivery of accurate and timely recommendations. Our GraphPro frameworkaddresses the challenge of evolving user preferences by seamlessly integratinga temporal prompt mechanism and a graph-structural prompt learning mechanisminto the pre-trained GNN model. The temporal prompt mechanism encodes timeinformation on user-item interaction, allowing the model to naturally capturetemporal context, while the graph-structural prompt learning mechanism enablesthe transfer of pre-trained knowledge to adapt to behavior dynamics without theneed for continuous incremental training. We further bring in a dynamicevaluation setting for recommendation to mimic real-world dynamic scenarios andbridge the offline-online gap to a better level. Our extensive experimentsincluding a large-scale industrial deployment showcases the lightweight plug-inscalability of our GraphPro when integrated with various state-of-the-artrecommenders, emphasizing the advantages of GraphPro in terms of effectiveness,robustness and efficiency.

GraphPro: Graph Pre-training and Prompt Learning for Recommendation