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。

图预训练与提示学习在推荐中的应用

Graph Pre-training and Prompt Learning for Recommendation

The behavioral dynamics of multi-agent systems have a rich and orderly
structure, which can be leveraged to understand these systems, and to improve
how artificial agents learn to operate in them. Here we introduce Relational
Forward Models (RFM) for multi-agent learning, networks that can learn to make
accurate predictions of agents' future behavior in multi-agent environments.
Because these models operate on the discrete entities and relations present in
the environment, they produce interpretable intermediate representations which
offer insights into what drives agents' behavior, and what events mediate the
intensity and valence of social interactions. Furthermore, we show that
embedding RFM modules inside agents results in faster learning systems compared
to non-augmented baselines. As more and more of the autonomous systems we
develop and interact with become multi-agent in nature, developing richer
analysis tools for characterizing how and why agents make decisions is
increasingly necessary. Moreover, developing artificial agents that quickly and
safely learn to coordinate with one another, and with humans in shared
environments, is crucial.

介绍了一种利用关系前向模型（RFM）来学习多智能体环境变化，预测代理机器的未来行为的算法。在多智能体环境中，这些模型产生可解释的中间表示，并提供对推动代理行为的因素、调节社交互动强度和价值的事件的见解。此外，将 RFM 模块嵌入代理中可导致更快的学习系统。