This repository contains the source code to generate the dataset and results presented our CIKM'25 publication "AOL4FOLTR: A Large-Scale Web Search Dataset for Federated Online Learning to Rank" [paper].
AOL4FOLTR is a dataset specifically tailored with its use in Federated Online Learning-to-Rank (short: FOLTR) in mind. It contains raw search queries and document contents, user IDs, and timestamps, based on AOL-IA, and originally, the 2006 AOL query logs. Furthermore, we generated top-20 result lists for each query, and designed 103 features to enable learning-to-rank.
- Download Dataset
- Generate Dataset
- How to Use Dataset
- Reproduce Results
- Learning-to-Rank Feature List
Our implementation of FPDGD is based on the code in https://github.com/ielab/fpdgd-ictir2021.