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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 201–250 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
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
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
Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts—0
Explicit and Implicit Semantic Ranking Framework—0
Deep Ranking Ensembles for Hyperparameter Optimization—0
Unbiased Learning to Rank with Biased Continuous Feedback—0
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation—0
Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities—0
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank—0
LaSER: Language-Specific Event RecommendationCode0
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue SystemsCode0
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search—0
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank—0
PASSerRank: Prediction of Allosteric Sites with Learning to RankCode0
Overcoming Prior Misspecification in Online Learning to RankCode0
Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation—0
CoSPLADE: Contextualizing SPLADE for Conversational Information RetrievalCode0
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank—0
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Multi-Task Off-Policy Learning from Bandit Feedback—0
Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems—0
Learning to Rank Graph-based Application Objects on Heterogeneous Memories—0
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance—0
Whole Page Unbiased Learning to Rank—0
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning—0
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model—0
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