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 GraphPL, 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 GraphPL 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 GraphPL when integrated with various state-of-the-art
recommenders, emphasizing the advantages of GraphPL in terms of effectiveness,
robustness and efficiency.

基于 GNN 的推荐系统可以通过多跳消息传递模型化复杂的用户 - 物品交互，但现有方法往往忽视用户 - 物品交互的动态性，限制了在实际动态环境下的可扩展性和性能。本研究提出了 GraphPL 框架，结合了高效的动态图预训练和提示学习，使 GNN 能够有效捕捉长期用户偏好和短期行为动态，从而提供准确及时的推荐。我们的 GraphPL 框架通过无缝集成的时间提示机制和图结构提示学习机制来解决用户偏好的变化挑战，同时不需要连续增量训练即可将预训练的知识转化为行为动态适应能力。我们还引入了动态评估设置来模拟真实动态场景，并在大规模工业部署中进行了大量实验证明了我们 GraphPL 的轻量级插件可扩展性，并强调 GraphPL 在效果、稳健性和效率方面的优势。