In the realm of personalized recommender systems, the challenge of adapting to evolving user preferences and the continuous influx of new users and items is paramount. Conventional models, typically reliant on a static training-test approach, struggle to keep pace with these dynamic demands. Streaming recommendation, particularly through continual graph learning, has emerged as a novel solution. However, existing methods in this area either rely on historical data replay, which is increasingly impractical due to stringent data privacy regulations; or are inability to effectively address the over-stability issue; or depend on model-isolation and expansion strategies. To tackle these difficulties, we present GPT4Rec, a Graph Prompt Tuning method for streaming Recommendation. Given the evolving user-item interaction graph, GPT4Rec first disentangles the graph patterns into multiple views. After isolating specific interaction patterns and relationships in different views, GPT4Rec utilizes lightweight graph prompts to efficiently guide the model across varying interaction patterns within the user-item graph. Firstly, node-level prompts are employed to instruct the model to adapt to changes in the attributes or properties of individual nodes within the graph. Secondly, structure-level prompts guide the model in adapting to broader patterns of connectivity and relationships within the graph. Finally, view-level prompts are innovatively designed to facilitate the aggregation of information from multiple disentangled views. These prompt designs allow GPT4Rec to synthesize a comprehensive understanding of the graph, ensuring that all vital aspects of the user-item interactions are considered and effectively integrated. Experiments on four diverse real-world datasets demonstrate the effectiveness and efficiency of our proposal.

翻译：在个性化推荐系统领域，适应不断演变的用户偏好以及持续涌入的新用户和新物品是一项关键挑战。传统模型通常依赖于静态的训练-测试方法，难以跟上这些动态需求。流式推荐，特别是通过持续图学习，已成为一种新颖的解决方案。然而，该领域的现有方法要么依赖历史数据回放（这在日益严格的数据隐私法规下越来越不切实际），要么无法有效解决过度稳定性问题，要么依赖于模型隔离与扩展策略。为应对这些困难，我们提出了GPT4Rec——一种面向流式推荐的图提示调优方法。针对不断演变的用户-物品交互图，GPT4Rec首先将图模式解耦为多个视图。在分离不同视图中的特定交互模式与关系后，GPT4Rec利用轻量级图提示来有效指导模型适应用户-物品图中变化的交互模式。具体而言：首先，节点级提示指导模型适应图中单个节点的属性或特征变化；其次，结构级提示引导模型适应图中更广泛的连接模式与关系；最后，创新设计的视图级提示促进从多个解耦视图中聚合信息。这些提示设计使GPT4Rec能够综合理解图结构，确保用户-物品交互的所有关键方面得到充分考虑与有效整合。在四个不同的真实数据集上的实验验证了所提方法的有效性与高效性。