mlsanityData to 7 Oct 2026

Introducing LETOR 4.0 Datasets

arXiv 1306.2597 ↗First posted 9 Jun 2013cs.IRcited by 120

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Abstract (arXiv)

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 ([email protected]). Our goal is to make the dataset reliable and useful for the community.

Key facts. Cited by 120 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2013.

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Citing papers per year: 2021: 14; 2022: 32; 2023: 16; 2024: 29; 2025: 17; 2026: 12.

120 papers.

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  1. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  2. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  3. 2022On Embeddings for Numerical Features in Tabular Deep Learningcited by 185
  4. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  5. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 114
  6. 2024TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensemblingcited by 105
  7. 2023TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023cited by 73
  8. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  9. 2022Neural Basis Models for Interpretabilitycited by 56
  10. 2022OpenFE: Automated Feature Generation with Expert-level Performancecited by 52
  11. 2022Revisiting Pretraining Objectives for Tabular Deep Learningcited by 43
  12. 2024SwitchTab: Switched Autoencoders Are Effective Tabular Learnerscited by 37
38 more
  1. 2021B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrievalcited by 25
  2. 2022Conformal Off-Policy Prediction in Contextual Banditscited by 25
  3. 2024Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noisecited by 25
  4. 2023A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learningcited by 23
  5. 2022Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergencecited by 21
  6. 2022Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rankcited by 17
  7. 2022Toward Understanding Privileged Features Distillation in Learning-to-Rankcited by 16
  8. 2022Distributed Differential Privacy in Multi-Armed Banditscited by 14
  9. 2023On Differentially Private Federated Linear Contextual Banditscited by 14
  10. 2021Control Variates for Slate Off-Policy Evaluationcited by 13
  11. 2022Predictive Inference with Weak Supervisioncited by 13
  12. 2022Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexitycited by 13
  13. 2024Offline Model-Based Optimization by Learning to Rankcited by 13
  14. 2024Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domainscited by 12
  15. 2022Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankingscited by 11
  16. 2022Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealerscited by 11
  17. 2022PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learningcited by 11
  18. 2023Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimizationcited by 11
  19. 2022Uncertainty Quantification for Fairness in Two-Stage Recommender Systemscited by 10
  20. 2022Multi-Label Learning to Rank through Multi-Objective Optimizationcited by 10
  21. 2023Optimization of Rank Losses for Image Retrievalcited by 10
  22. 2024Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Datasetcited by 10
  23. 2022OCD: Learning to Overfit with Conditional Diffusion Modelscited by 9
  24. 2022Towards Disentangling Relevance and Bias in Unbiased Learning to Rankcited by 9
  25. 2023RankFormer: Listwise Learning-to-Rank Using Listwide Labelscited by 9
  26. 2024Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.Icited by 9
  27. 2025Breaking the Top-K Barrier: Advancing Top-K Ranking Metrics Optimization in Recommender Systemscited by 9
  28. 2022Hierarchical Average Precision Training for Pertinent Image Retrievalcited by 8
  29. 2024Arithmetic Feature Interaction Is Necessary for Deep Tabular Learningcited by 8
  30. 2024Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Searchcited by 8
  31. 2021Individually Fair Rankingcited by 7
  32. 2022Interpretability with full complexity by constraining feature informationcited by 7
  33. 2024Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rankcited by 7
  34. 2021Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rankcited by 6
  35. 2021PairRank: Online Pairwise Learning to Rank by Divide-and-Conquercited by 6
  36. 2023Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systemscited by 6
  37. 2024Two-stage Risk Control with Application to Ranked Retrievalcited by 6
  38. 2022Learning Distributionally Robust Models at Scale via Composite Optimizationcited by 5

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