评估检索系统随时间推移的表现变化，揭示其长期有效性。
LongEval at CLEF 2025: Longitudinal Evaluation of IR Systems on Web and Scientific Data
- 构建动态数据集，模拟文档、查询与相关性随时间演变。
- 19支队伍参与，使用nDCG等指标量化效果随时间的变化。
- 适合关注系统长期性能评估的研究者与工业界应用者。
LongEval实验室专注于信息检索系统在时间维度上的评估。本次提供两个数据集，捕捉随时间演化的搜索场景，包括不断变化的文档、查询和相关性标注。系统评估从时间视角出发，即考察在数据动态变化下检索效果的表现。本届为第三届，包含两项任务：一项针对即席网络检索，另一项聚焦科学论文检索。本文概述了今年的任务设置与数据集，并介绍参与系统的概况。共19支团队提交方法，采用nDCG及多种衡量检索效果随时间变化的指标进行评估。
原文摘要 · Abstract (English)
The LongEval lab focuses on the evaluation of information retrieval systems over time. Two datasets are provided that capture evolving search scenarios with changing documents, queries, and relevance assessments. Systems are assessed from a temporal perspective-that is, evaluating retrieval effectiveness as the data they operate on changes. In its third edition, LongEval featured two retrieval tasks: one in the area of ad-hoc web retrieval, and another focusing on scientific article retrieval. We present an overview of this year's tasks and datasets, as well as the participating systems. A total of 19 teams submitted their approaches, which we evaluated using nDCG and a variety of measures that quantify changes in retrieval effectiveness over time.
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