{"ID":2840938,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12495","arxiv_id":"2511.12495","title":"Task-Aware Retrieval Augmentation for Dynamic Recommendation","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.","short_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 GNN...","url_abs":"https://arxiv.org/abs/2511.12495","url_pdf":"https://arxiv.org/pdf/2511.12495v1","authors":"[\"Zhen Tao\",\"Xinke Jiang\",\"Qingshuai Feng\",\"Haoyu Zhang\",\"Lun Du\",\"Yuchen Fang\",\"Hao Miao\",\"Bangquan Xie\",\"Qingqiang Sun\"]","published":"2025-11-16T08:14:52Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.SI\"]","methods":"[\"Graph Neural Network\",\"Transformer\"]","has_code":false}