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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 551–600 of 753 papers

TitleStatusHype
Modeling Document Interactions for Learning to Rank with Regularized Self-Attention—0
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm—0
Model Spider: Learning to Rank Pre-Trained Models Efficiently—0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank—0
MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting—0
MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model—0
MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?—0
Multi-Label Learning to Rank through Multi-Objective Optimization—0
Multi-objective Learning to Rank by Model Distillation—0
Multi-Task Off-Policy Learning from Bandit Feedback—0
Multivariate Spearman's rho for aggregating ranks using copulas—0
Neural Attention for Learning to Rank Questions in Community Question Answering—0
Neural Feature Selection for Learning to Rank—0
Neural Models for Information Retrieval—0
Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees—0
Neural Ranking Models with Multiple Document Fields—0
News Citation Recommendation with Implicit and Explicit Semantics—0
Noise tolerance of learning to rank under class-conditional label noise—0
Non-convex Regularizations for Feature Selection in Ranking With Sparse SVM—0
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation—0
NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models—0
Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities—0
Offline Learning for Combinatorial Multi-armed Bandits—0
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model—0
On Application of Learning to Rank for E-Commerce Search—0
On Learning to Rank Long Sequences with Contextual Bandits—0
Online Diverse Learning to Rank from Partial-Click Feedback—0
Online Learning of Optimally Diverse Rankings—0
Online Learning to Rank in Stochastic Click Models—0
Online Learning to Rank with Features—0
Online Learning to Rank with Feedback at the Top—0
Online Learning to Rank with Top-k Feedback—0
On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank—0
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search—0
On the Consistency of AUC Pairwise Optimization—0
On the ERM Principle with Networked Data—0
On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons—0
Ontology-Based Retrieval \& Neural Approaches for BioASQ Ideal Answer Generation—0
OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation—0
Optimize What You Evaluate With: A Simple Yet Effective Framework For Direct Optimization Of IR Metrics—0
Optimizing Preference Alignment with Differentiable NDCG Ranking—0
Optimizing Ranking Models in an Online Setting—0
Optimizing Ranking Systems Online as Bandits—0
Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims—0
Pairwise Judgment Formulation for Semantic Embedding Model in Web Search—0
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions—0
Par4Sim -- Adaptive Paraphrasing for Text Simplification—0
Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems—0
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank—0
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