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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 71–80 of 753 papers

TitleStatusHype
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
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
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