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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 1–50 of 753 papers

TitleStatusHype
Kamae: Bridging Spark and Keras for Seamless ML PreprocessingCode1
Towards Two-Stage Counterfactual Learning to Rank—0
Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to RankCode0
LTRR: Learning To Rank Retrievers for LLMsCode0
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal DiscoveryCode0
Bounded-Abstention Pairwise Learning to Rank—0
Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision—0
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to RankCode2
Unlearning for Federated Online Learning to Rank: A Reproducibility StudyCode0
Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation—0
Beyond Pairwise Learning-To-Rank At Airbnb—0
A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao—0
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-SupervisionCode0
FAIR-QR: Enhancing Fairness-aware Information Retrieval through Query Refinement—0
HAPI: A Model for Learning Robot Facial Expressions from Human PreferencesCode0
Long Context Modeling with Ranked Memory-Augmented Retrieval—0
Weak Supervision for Improved Precision in Search Systems—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and RelevanceCode0
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVMCode0
Cascading Bandits Robust to Adversarial Corruptions—0
Offline Learning for Combinatorial Multi-armed Bandits—0
Reqo: A Robust and Explainable Query Optimization Cost Model—0
RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question AnsweringCode0
Learning to Rank Aspects and Opinions for Comparative ExplanationsCode0
Efficient support ticket resolution using Knowledge Graphs—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Effective and secure federated online learning to rank—0
Ranking-aware adapter for text-driven image ordering with CLIPCode1
Learning Cluster Representatives for Approximate Nearest Neighbor SearchCode0
GABAR: Graph Attention-Based Action Ranking for Relational Policy Learning—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
ICLERB: In-Context Learning Embedding and Reranker Benchmark—0
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System—0
A Survey on E-Commerce Learning to Rank—0
TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman ModelCode1
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
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation SystemsCode1
Offline Model-Based Optimization by Learning to RankCode1
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
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