Dynamic recommendation systems aim to provide personalized suggestions by modeling temporal user-item interactions across time-series behavioral data. Recent studies have leveraged pre-trained dynamic graph neural networks (GNNs) to learn user-item representations over temporal snapshot graphs. However, fine-tuning GNNs on these graphs often results in generalization issues due to temporal discrepancies between pre-training and fine-tuning stages, limiting the model's ability to capture evolving user preferences. To address this, we propose TarDGR, a task-aware retrieval-augmented framework designed to enhance generalization capability by incorporating task-aware model and retrieval-augmentation. Specifically, TarDGR introduces a Task-Aware Evaluation Mechanism to identify semantically relevant historical subgraphs, enabling the construction of task-specific datasets without manual labeling. It also presents a Graph Transformer-based Task-Aware Model that integrates semantic and structural encodings to assess subgraph relevance. During inference, TarDGR retrieves and fuses task-aware subgraphs with the query subgraph, enriching its representation and mitigating temporal generalization issues. Experiments on multiple large-scale dynamic graph datasets demonstrate that TarDGR consistently outperforms state-of-the-art methods, with extensive empirical evidence underscoring its superior accuracy and generalization capabilities.

翻译：动态推荐系统旨在通过建模时间序列行为数据中的时序用户-物品交互，提供个性化推荐。近期研究利用预训练的动态图神经网络（GNNs）在时序快照图上学习用户-物品表示。然而，在这些图上微调GNNs常因预训练与微调阶段间的时序差异导致泛化问题，限制了模型捕捉用户偏好演变的能力。为解决此问题，我们提出TarDGR，一种任务感知检索增强框架，通过结合任务感知模型与检索增强机制来提升泛化能力。具体而言，TarDGR引入任务感知评估机制以识别语义相关的历史子图，从而无需人工标注即可构建任务特定数据集。该框架还提出基于图Transformer的任务感知模型，整合语义与结构编码以评估子图相关性。在推理阶段，TarDGR检索任务感知子图并与查询子图融合，丰富其表示并缓解时序泛化问题。在多个大规模动态图数据集上的实验表明，TarDGR持续优于现有最优方法，大量实证证据凸显了其卓越的准确性与泛化能力。