Task-Aware Retrieval Augmentation for Dynamic Recommendation

• URL: http://arxiv.org/abs/2511.12495v1
• Date: Sun, 16 Nov 2025 08:14:52 GMT
• Title: Task-Aware Retrieval Augmentation for Dynamic Recommendation
• Authors: Zhen Tao, Xinke Jiang, Qingshuai Feng, Haoyu Zhang, Lun Du, Yuchen Fang, Hao Miao, Bangquan Xie, Qingqiang Sun,
• Abstract summary: TarDGR is a task-aware retrieval-augmented framework designed to enhance generalization capability.<n>It identifies semantically relevant historical subgraphs, enabling the construction of task-specific datasets without manual labeling.<n>Experiments on multiple large-scale dynamic graph datasets demonstrate that TarDGR consistently outperforms state-of-the-art methods.
• Score: 30.33604295626554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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