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