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Learning-To-Rank

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).

Papers

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Showing 151–175 of 753 papers

TitleStatusHype
Inference-time Stochastic Ranking with Risk Control—0
Skellam Rank: Fair Learning to Rank Algorithm Based on Poisson Process and Skellam Distribution for Recommender Systems—0
RankFormer: Listwise Learning-to-Rank Using Listwide LabelsCode1
Model Spider: Learning to Rank Pre-Trained Models Efficiently—0
LibAUC: A Deep Learning Library for X-Risk OptimizationCode2
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions—0
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models—0
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking—0
Adversarial Attacks on Online Learning to Rank with Click Feedback—0
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach—0
RankCSE: Unsupervised Sentence Representations Learning via Learning to RankCode1
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to RankCode0
Learning to Rank Utterances for Query-Focused Meeting Summarization—0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
Selective Query Processing: a Risk-Sensitive Selection of System Configurations—0
Unconfounded Propensity Estimation for Unbiased Ranking—0
Efficient and Effective Tree-based and Neural Learning to Rank—0
THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case RetrievalCode1
THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case EntailmentCode1
Position Bias Estimation with Item Embedding for Sparse Dataset—0
Ranking & Reweighting Improves Group Distributional Robustness—0
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
On the Impact of Outlier Bias on User ClicksCode0
Learning to Re-rank with Constrained Meta-Optimal Transport—0
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