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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 101–150 of 753 papers

TitleStatusHype
An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking—0
Learning to Rank Salient Content for Query-focused Summarization—0
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
Combinatorial Logistic BanditsCode0
Optimizing Preference Alignment with Differentiable NDCG Ranking—0
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework—0
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
Scale-Invariant Learning-to-Rank—0
Meta Learning to Rank for Sparsely Supervised Queries—0
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation—0
Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study—0
Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)—0
Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)—0
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs—0
Patch Ranking: Efficient CLIP by Learning to Rank Local PatchesCode0
Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models—0
A Framework for Ranking Content Providers Using Prompt Engineering and Self-Attention Network—0
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank—0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
Zeroshot Listwise Learning to Rank Algorithm for Recommendation—0
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling—0
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations—0
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank—0
Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending—0
Pairwise Judgment Formulation for Semantic Embedding Model in Web Search—0
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsCode0
Set2Seq Transformer: Learning Permutation Aware Set Representations of Artistic Sequences—0
Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential RecommendationCode0
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank—0
Hidden or Inferred: Fair Learning-To-Rank with Unknown DemographicsCode0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Multi-objective Learning to Rank by Model Distillation—0
Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering PlatformsCode0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
When Search Engine Services meet Large Language Models: Visions and Challenges—0
Learning to Rank for Maps at Airbnb—0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback—0
MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model—0
Towards Explainable Test Case Prioritisation with Learning-to-Rank Models—0
GotFunding: A grant recommendation system based on scientific articles—0
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems—0
Metalearners for Ranking Treatment Effects—0
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank—0
Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations—0
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted TreesCode0
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor SearchCode0
Learning to rank quantum circuits for hardware-optimized performance enhancement—0
Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation—0
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