LongEval at CLEF 2025：面向网络与科学数据的检索系统纵向评估
LongEval at CLEF 2025: Longitudinal Evaluation of IR Systems on Web and Scientific Data
AI总结：
LongEval 2025 通过两个动态数据集和两项检索任务，从时间维度评估检索系统在数据变化下的效果稳定性，并基于 nDCG 等指标对 19 支参赛团队的方法进行了纵向评测。
摘要：
LongEval 实验室专注于信息检索系统随时间变化的评估。该实验室提供了两个数据集，用于捕捉不断演化的搜索场景，其中文档、查询和相关性判断均会发生变化。系统从时间维度进行评估，即在系统所处理的数据发生变化时，衡量其检索效果的变化。在第三届活动中，LongEval 设置了两项检索任务：一项是临时网络检索，另一项是科学文章检索。本文概述了今年的任务和数据集，以及参与系统的总体情况。共有 19 支团队提交了各自的方法，我们使用 nDCG 以及多种度量指标对这些方法进行了评估，这些指标用于量化检索效果随时间的变化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
