AOL4FOLTR

AOL4FOLTR is the first learning-to-rank (LTR) dataset designed specifically for evaluating federated online learning-to-rank (FOLTR) algorithms. Including user identifiers and timestamps, this dataset allows for the simulation of real user behavior with heterogeneous data and in asynchronous federated learning settings. The dataset consists of two files letor.txt.gz (55G uncompressed) metadata.csv letor.txt contains the query-document pairs for all query logs in standard LETOR format. Each query-document pair holds a binary label derived from user clicks, and is further represented by a 103-dimensional vector. We document the features in our code repository. The query logs are cross-referenced (by qid) in metadata.csv, where contextual information is provided. This includes the user, timestamp, raw query, the target document ID, and a list of 20 candidate documents. The document IDs and user IDs directly map to the AOL-IA dataset; the query IDs do not. For access to the raw document contents, please refer to this dataset.AOL4FOLTR 是首个专门为评估联邦在线排序学习（federated online learning-to-rank, FOLTR）算法而设计的排序学习（learning-to-rank, LTR）数据集。 该数据集包含用户标识符与时间戳，可支持在异构数据与异步联邦学习场景下模拟真实用户行为。 本数据集包含两个文件： letor.txt.gz（解压后大小为55GB） metadata.csv letor.txt 以标准 LETOR 格式存储所有查询日志中的查询-文档对。每组查询-文档对均带有源自用户点击的二元标签，并通过103维向量进行表征，相关特征的详细说明已收录于本项目的代码仓库中。 查询日志通过 metadata.csv 中的 qid 字段进行交叉关联，该文件提供了各类上下文信息，包括用户信息、时间戳、原始查询文本、目标文档ID以及包含20个候选文档的列表。 文档ID与用户ID可直接映射至 AOL-IA 数据集，但查询ID无法直接完成映射。若需获取原始文档内容，请参考该数据集。