GNN-based recommenders have excelled in modeling intricate user-item interactions through multi-hop message passing. However, existing methods often overlook the dynamic nature of evolving user-item interactions, which impedes the adaption to changing user preferences and distribution shifts in newly arriving data. Thus, their scalability and performances in real-world dynamic environments are limited. In this study, we propose GraphPro, a framework that incorporates parameter-efficient and dynamic graph pre-training with prompt learning. This novel combination empowers GNNs to effectively capture both long-term user preferences and short-term behavior dynamics, enabling the delivery of accurate and timely recommendations. Our GraphPro framework addresses the challenge of evolving user preferences by seamlessly integrating a temporal prompt mechanism and a graph-structural prompt learning mechanism into the pre-trained GNN model. The temporal prompt mechanism encodes time information on user-item interaction, allowing the model to naturally capture temporal context, while the graph-structural prompt learning mechanism enables the transfer of pre-trained knowledge to adapt to behavior dynamics without the need for continuous incremental training. We further bring in a dynamic evaluation setting for recommendation to mimic real-world dynamic scenarios and bridge the offline-online gap to a better level. Our extensive experiments including a large-scale industrial deployment showcases the lightweight plug-in scalability of our GraphPro when integrated with various state-of-the-art recommenders, emphasizing the advantages of GraphPro in terms of effectiveness, robustness and efficiency.

翻译：基于GNN的推荐系统通过多跳消息传递在建模复杂的用户-物品交互方面表现出色。然而，现有方法往往忽略用户-物品交互的演变动态性，这阻碍了其适应不断变化的用户偏好以及新到达数据中的分布偏移。因此，这些方法在真实动态环境中的可扩展性和性能受到限制。在本研究中，我们提出GraphPro框架，该框架将参数高效的动态图预训练与提示学习相结合。这一新颖组合使GNN能够有效捕捉长期用户偏好与短期行为动态，从而提供准确且及时的推荐。我们的GraphPro框架通过将时序提示机制和图结构提示学习机制无缝集成到预训练的GNN模型中，解决了用户偏好演变带来的挑战。时序提示机制对用户-物品交互中的时间信息进行编码，使模型能够自然捕捉时序上下文；而图结构提示学习机制则实现了预训练知识的迁移，使其适应行为动态，而无需持续增量训练。此外，我们引入了一种动态评估设置用于推荐，以模拟真实动态场景，并更好地弥合离线和在线之间的差距。我们的大规模实验（包括工业级部署）展示了GraphPro在与多种最先进推荐系统集成时轻量级即插即用的可扩展性，凸显了GraphPro在有效性、鲁棒性和效率方面的优势。