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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 201–225 of 753 papers

TitleStatusHype
Model Spider: Learning to Rank Pre-Trained Models Efficiently—0
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
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach—0
Adversarial Attacks on Online Learning to Rank with Click Feedback—0
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
Unconfounded Propensity Estimation for Unbiased Ranking—0
Selective Query Processing: a Risk-Sensitive Selection of System Configurations—0
Efficient and Effective Tree-based and Neural Learning to Rank—0
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
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk MinimizationCode0
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training—0
An Offline Metric for the Debiasedness of Click ModelsCode0
Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval—0
Learning To Rank Resources with GNN—0
Metric-agnostic Ranking Optimization—0
OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation—0
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