让搜索结果既准确又新鲜，动态平衡相关性与时效性。
Re3: Learning to Balance Relevance & Recency for Temporal Information Retrieval
- 用查询感知门控机制动态融合语义与时间信息
- 在三个子集上均实现R@1最优，超越现有方法
- 适合需要精准时序检索的搜索系统开发者
时间信息检索（TIR）是现代搜索系统中的关键但未完全解决的任务，需同时满足查询的信息需求和时间约束。该任务面临两大挑战：相关性（符合查询的时间要求）与时效性（选择最新版本文档）。现有方法常孤立处理二者，依赖脆弱启发式规则，在时间需求与过时抗性交织场景中表现不佳。为此，我们提出Re2Bench基准，专门用于解耦并评估相关性、时效性及其组合。在此基础上，我们设计了Re3——一个统一且轻量的框架，通过查询感知门控机制动态平衡语义与时间信息。在Re2Bench上，Re3在所有三个子集的R@1指标上均达到领先水平。消融实验与主干模型敏感性测试证实其鲁棒性，展现出在多种编码器及真实场景下的强泛化能力。本工作提供可推广的解决方案与严谨评估体系，推动时序感知检索系统发展。Re3与Re2Bench已公开：https://anonymous.4open.science/r/Re3-0C5A
原文摘要 · Abstract (English)
Temporal Information Retrieval (TIR) is a critical yet unresolved task for modern search systems, retrieving documents that not only satisfy a query's information need but also adhere to its temporal constraints. This task is shaped by two challenges: Relevance, ensuring alignment with the query's explicit temporal requirements, and Recency, selecting the freshest document among multiple versions. Existing methods often address the two challenges in isolation, relying on brittle heuristics that fail in scenarios where temporal requirements and staleness resistance are intertwined. To address this gap, we introduce Re2Bench, a benchmark specifically designed to disentangle and evaluate Relevance, Recency, and their hybrid combination. Building on this foundation, we propose Re3, a unified and lightweight framework that dynamically balances semantic and temporal information through a query-aware gating mechanism. On Re2Bench, Re3 achieves state-of-the-art results, leading in R@1 across all three subsets. Ablation studies with backbone sensitivity tests confirm robustness, showing strong generalization across diverse encoders and real-world settings. This work provides both a generalizable solution and a principled evaluation suite, advancing the development of temporally aware retrieval systems. Re3 and Re2Bench are available online: https://anonymous.4open.science/r/Re3-0C5A
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