Re-ranking systems aim to reorder an initial list of documents to satisfy better the information needs associated with a user-provided query. Modern re-rankers predominantly rely on neural network models, which have proven highly effective in representing samples from various modalities. However, these models typically evaluate query-document pairs in isolation, neglecting the underlying document distribution that could enhance the quality of the re-ranked list. To address this limitation, we propose Graph Neural Re-Ranking (GNRR), a pipeline based on Graph Neural Networks (GNNs), that enables each query to consider documents distribution during inference. Our approach models document relationships through corpus subgraphs and encodes their representations using GNNs. Through extensive experiments, we demonstrate that GNNs effectively capture cross-document interactions, improving performance on popular ranking metrics. In TREC-DL19, we observe a relative improvement of 5.8% in Average Precision compared to our baseline. These findings suggest that integrating the GNN segment offers significant advantages, especially in scenarios where understanding the broader context of documents is crucial.

翻译：重排序系统旨在对初始文档列表进行重新排序，以更好地满足用户提供查询所关联的信息需求。现代重排序器主要依赖神经网络模型，这些模型已被证明在表示多模态样本方面非常有效。然而，这些模型通常孤立地评估查询-文档对，忽略了可能提升重排序列表质量的底层文档分布。为应对这一局限，我们提出图神经重排序（GNRR），一种基于图神经网络（GNNs）的流程，使每个查询能在推理过程中考虑文档分布。我们的方法通过语料子图建模文档关系，并利用GNNs编码其表示。通过大量实验，我们证明GNNs能有效捕捉跨文档交互，从而在主流排序指标上提升性能。在TREC-DL19数据集上，相较于基线模型，我们观察到平均精度实现了5.8%的相对提升。这些发现表明，整合GNN模块具有显著优势，尤其在理解文档更广泛上下文至关重要的场景中。