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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
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMsCode0
Ranking Structured Objects with Graph Neural NetworksCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Reinforcement Online Learning to Rank with Unbiased Reward ShapingCode0
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to RankCode0
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk MinimizationCode0
HAPI: A Model for Learning Robot Facial Expressions from Human PreferencesCode0
Groupwise Query Performance Prediction with BERTCode0
Hashing as Tie-Aware Learning to RankCode0
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
Learning Cluster Representatives for Approximate Nearest Neighbor SearchCode0
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative StudyCode0
Policy-Gradient Training of Fair and Unbiased Ranking FunctionsCode0
Support vector comparison machinesCode0
Content Selection for Real-time Sports News Construction from Commentary Texts—0
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet—0
A scale invariant ranking function for learning-to-rank: a real-world use case—0
Constrained Multi-Task Learning for Automated Essay Scoring—0
Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank—0
ARSM Gradient Estimator for Supervised Learning to Rank—0
A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization—0
Computational and Statistical Tradeoffs in Learning to Rank—0
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain—0
A Representation Theory for Ranking Functions—0
Community-based Cyberreading for Information Understanding—0
Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)—0
Communication-Efficient Algorithms for Statistical Optimization—0
Are Neural Ranking Models Robust?—0
A Deep Investigation of Deep IR Models—0
GABAR: Graph Attention-Based Action Ranking for Relational Policy Learning—0
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems—0
FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise—0
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context—0
From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews—0
Forest Reranking through Subtree Ranking—0
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Generalization error bounds for learning to rank: Does the length of document lists matter?—0
Click-aware purchase prediction with push at the top—0
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting—0
Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities—0
Global Ranking Using Continuous Conditional Random Fields—0
GotFunding: A grant recommendation system based on scientific articles—0
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts—0
Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016—0
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph—0
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment—0
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection—0
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking—0
Federated Unbiased Learning to Rank—0
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