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：基于图神经网络（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，现有方法常忽略用户-物品交互演化的动态特性，这阻碍了模型对用户偏好变化和新到达数据分布偏移的适应能力。因此，其在真实动态环境中的可扩展性和性能受限。本研究提出GraphPro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，从而提供精确且及时的推荐。我们的GraphPro框架通过将时间提示机制和图结构提示学习机制无缝集成到预训练GNN模型中，解决了用户偏好演化的挑战。时间提示机制对用户-物品交互的时间信息进行编码，使模型自然捕捉时间上下文；而图结构提示学习机制则能迁移预训练知识以适应行为动态，无需持续增量训练。我们进一步引入面向推荐的动态评估设置，以模拟真实动态场景，并更好地弥合离线与在线性能差距。包括大规模工业部署在内的广泛实验表明，GraphPro在与多种最先进推荐系统集成时，展现出轻量级即插即用的可扩展性，凸显了其在有效性、鲁棒性和效率方面的优势。