Learning-To-Rank
180 papers with code • 0 benchmarks • 9 datasets
Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).
Benchmarks
These leaderboards are used to track progress in Learning-To-Rank
Libraries
Use these libraries to find Learning-To-Rank models and implementationsDatasets
MSLR-WEB10K
Learning to Rank Challenge
ReQA
Latest papers
THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment
lihaitao18375278/THUIR-COLIEE2023
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This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task.
On the Impact of Outlier Bias on User Clicks
We therefore propose an outlier-aware click model that accounts for both outlier and position bias, called outlier-aware position-based model ( OPBM).
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization
For the CLTR field, our novel exposure-based risk minimization method enables practitioners to adopt CLTR methods in a safer manner that mitigates many of the risks attached to previous methods.
THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank
This paper introduces the approaches we have used to participate in the WSDM Cup 2023 Task 1: Unbiased Learning to Rank.
An Offline Metric for the Debiasedness of Click Models
We prove that debiasedness is a necessary condition for recovering unbiased and consistent relevance scores and for the invariance of click prediction under covariate shift.
Deep Ranking Ensembles for Hyperparameter Optimization
releaunifreiburg/deeprankingensembles
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Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI.
LaSER: Language-Specific Event Recommendation
This article introduces the novel task of language-specific event recommendation, which aims to recommend events relevant to the user query in the language-specific context.
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems
Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied.
Lero: A Learning-to-Rank Query Optimizer
blondig/lero-on-postgresql
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In this paper, we introduce a learning-to-rank query optimizer, called Lero, which builds on top of a native query optimizer and continuously learns to improve the optimization performance.
PASSerRank: Prediction of Allosteric Sites with Learning to Rank
One of the major challenges in allosteric drug research is the identification of allosteric sites.
