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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 51–100 of 753 papers

TitleStatusHype
Controlling Fairness and Bias in Dynamic Learning-to-RankCode1
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and WildCode1
L2R2: Leveraging Ranking for Abductive ReasoningCode1
Accelerated Convergence for Counterfactual Learning to RankCode1
Context-Aware Learning to Rank with Self-AttentionCode1
That is a Known Lie: Detecting Previously Fact-Checked ClaimsCode1
RaCT: Toward Amortized Ranking-Critical Training For Collaborative FilteringCode1
Hierarchical Entity Typing via Multi-level Learning to RankCode1
TREC CAsT 2019: The Conversational Assistance Track OverviewCode1
Gradient Boosting Neural Networks: GrowNetCode1
Selective Weak Supervision for Neural Information RetrievalCode1
Listwise Learning to Rank by Exploring Unique RatingsCode1
Learning to Blindly Assess Image Quality in the Laboratory and WildCode1
Towards Amortized Ranking-Critical Training for Collaborative FilteringCode1
Learning Groupwise Multivariate Scoring Functions Using Deep Neural NetworksCode1
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic ClusteringCode1
Learning Latent Vector Spaces for Product SearchCode1
Introducing LETOR 4.0 DatasetsCode1
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
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
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
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