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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 701–750 of 753 papers

TitleStatusHype
Semi-Automatic Construction of Word-Formation Networks (for Polish and Spanish)—0
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs—0
Position Bias Estimation for Unbiased Learning-to-Rank in eCommerce Search—0
Direct Learning to Rank and Rerank—0
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling—0
Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts—0
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents—0
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
Don't Mention the Shoe! A Learning to Rank Approach to Content Selection for Image Description Generation—0
Separate and Attend in Personal Email Search—0
Drug Selection via Joint Push and Learning to Rank—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao—0
ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking—0
Effective and secure federated online learning to rank—0
Efficient and Accurate Top-K Recovery from Choice Data—0
Efficient and Consistent Adversarial Bipartite Matching—0
Efficient and Effective Tree-based and Neural Learning to Rank—0
Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations—0
Efficient Collective Entity Linking with Stacking—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
Automated Essay Scoring by Maximizing Human-Machine Agreement—0
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation—0
Efficient support ticket resolution using Knowledge Graphs—0
EILEEN: A recommendation system for scientific publications and grants—0
Eliminating Search Intent Bias in Learning to Rank—0
Embedding Meta-Textual Information for Improved Learning to Rank—0
End-to-end Learning for Fair Ranking Systems—0
Automated Disease Normalization with Low Rank Approximations—0
autoBagging: Learning to Rank Bagging Workflows with Metalearning—0
Enhancing LambdaMART Using Oblivious Trees—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
Set2Seq Transformer: Learning Permutation Aware Set Representations of Artistic Sequences—0
Estimating Position Bias without Intrusive Interventions—0
Valid Explanations for Learning to Rank Models—0
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths—0
Variance Reduction in Gradient Exploration for Online Learning to Rank—0
Expected Divergence Based Feature Selection for Learning to Rank—0
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency—0
Similarity Learning on an Explicit Polynomial Kernel Feature Map for Person Re-Identification—0
Explicit and Implicit Semantic Ranking Framework—0
Simple to Complex Cross-modal Learning to Rank—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
Explore Entity Embedding Effectiveness in Entity Retrieval—0
Influence of Neighborhood on the Preference of an Item in eCommerce Search—0
Extraction of Domain-Specific Bilingual Lexicon from Comparable Corpora: Compositional Translation and Ranking—0
Extractive Headline Generation Based on Learning to Rank for Community Question Answering—0
Extreme Learning to Rank via Low Rank Assumption—0
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