Overview
The full Istella Learning to Rank dataset was used to train a stage of the Istella production ranking pipeline. It supports large-scale experiments on ranking effectiveness, efficiency and scalability.
- 33,018 queries; 220 features per query-document pair
- 10,454,629 examples; approximately 316 examples per query
- Relevance labels from 0 to 4
- Training/test split: 80% / 20%
How to cite this dataset
If you use this dataset in your research, please acknowledge Istella SpA and cite the following publication:
Domenico Dato, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini. 2016. Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees. ACM Transactions on Information Systems. 10.1145/2987380.
BibTeX
@article{istellaLetor2016,
author = {Domenico Dato and Claudio Lucchese and Franco Maria Nardini and Salvatore Orlando and Raffaele Perego and Nicola Tonellotto and Rossano Venturini},
title = {Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees},
journal = {ACM Transactions on Information Systems},
year = {2016},
doi = {10.1145/2987380},
url = {https://doi.org/10.1145/2987380},
volume = {35},
number = {2},
articleno = {15},
numpages = {31},
}
Download
The dataset is available solely for non-commercial use. Read and accept the license before downloading the files. Read the license.