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Introducing LETOR 4.0 Datasets
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LETOR is a package of benchmark data sets for research on LEarning TO Rank, which contains standard features, relevance judgments, data partitioning, evaluation tools, and several baselines. Version 1.0 was released in April 2007. Version 2.0 was released in Dec. 2007. Version 3.0 was released in Dec. 2008. This version, 4.0, was released in July 2009. Very different from previous versions (V3.0 is an update based on V2.0 and V2.0 is an update based on V1.0), LETOR4.0 is a totally new release. It uses the Gov2 web page collection (~25M pages) and two query sets from Million Query track of TREC 2007 and TREC 2008. We call the two query sets MQ2007 and MQ2008 for short. There are about 1700 queries in MQ2007 with labeled documents and about 800 queries in MQ2008 with labeled documents. If you have any questions or suggestions about the datasets, please kindly email us (letor@microsoft.com). Our goal is to make the dataset reliable and useful for the community.
Forward citations
Cited by 11 Pith papers
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Efficient and Robust Online Learning to Rank in Decentralized Systems
RankGuard is a decentralized OLTR system that filters model updates using local click data for poisoning resistance and supplies the first formal convergence guarantee for decentralized OLTR.
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Improved Algorithms for Nash Welfare in Linear Bandits
FairLinBandit achieves order-optimal Nash regret Õ(d/√T) and the first sublinear p-mean regret bounds in linear bandits for every real p.
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Structure-aware Relative Policy Optimization for Ranking
Dividing pairwise reward differences by top-weighted Kendall-tau distance in group-relative policy optimization improves listwise ranking performance.
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An Epistemic Position-Based Click Model: From Interactions to Epistemic Distributions of Relevance and Bias
A position-based click model that outputs Beta distributions over relevance and position-bias parameters, trained with self-normalizing and position-conditioned estimators, outperforms a pointwise PBM at predicting si...
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Exposure-Based Reinforcement Learning to Rank
An exposure-based policy-gradient estimator for ranking is proposed; the appendix retracts the key derivation, and the reported experiments were not rerun with the corrected estimator.
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Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation
A new listwise learning-to-rank method uses smooth rank approximation and boosting to optimize without depending on a single metric.
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From Noise to Order: Learning to Rank via Denoising Diffusion
DiffusionRank, a diffusion-based generative model over feature-label tuples, improves learning-to-rank over discriminative baselines on MQ2007 and MSLR-WEB10K, but not consistently on MQ2008.
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Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank (Extended Abstract)
Two-tower models are identifiable without swaps when feature distributions overlap across positions, and strong logging policies degrade them only through model misspecification.
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Representation Curriculum: Stagewise Training for Robust Ranking and Allocation
Representation Curriculum stages feature introduction during training to prioritize content merit signals over exposure-dependent ones, reducing shortcut learning and improving cold-start robustness in ranking.
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Beyond Exposure: Optimizing Ranking Fairness with Non-linear Time-Income Functions
DIDRF optimizes ranking so cumulative provider income, not just exposure, is proportional to relevance under time-dependent exposure-to-income functions.
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On the importance of multiple training seeds for evaluating machine unlearning
Machine-unlearning evaluation with a single training seed can misrepresent method performance, particularly for deterministic unlearning methods, and extra unlearning seeds do not fix it.
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