SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–350 of 753 papers

TitleStatusHype
Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces—0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
Learning to rank for uplift modeling—0
LDTM: A Latent Document Type Model for Cumulative Citation Recommendation—0
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System—0
Deep Multi-view Learning to Rank—0
Individually Fair Ranking—0
Learning diverse rankings with multi-armed bandits—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Learning Efficient Anomaly Detectors from K-NN Graphs—0
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages—0
Learning from User Interactions with Rankings: A Unification of the Field—0
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion—0
Learning Hybrid Representations to Retrieve Semantically Equivalent Questions—0
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs—0
Learning Modulo Theories for preference elicitation in hybrid domains—0
Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval—0
Learning Neural Ranking Models Online from Implicit User Feedback—0
Learning Optimal Card Ranking from Query Reformulation—0
Learning Paraphrasing for Multiword Expressions—0
Learning Rank Functionals: An Empirical Study—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
Learning Term Weights for Ad-hoc Retrieval—0
Learning the Peculiar Value of Actions—0
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects—0
Position Bias Estimation with Item Embedding for Sparse Dataset—0
Learning-to-Count by Learning-to-Rank: Weakly Supervised Object Counting & Localization Using Only Pairwise Image Rankings—0
Learning to Differentiate Better from Worse Translations—0
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events—0
A General Framework for Counterfactual Learning-to-Rank—0
A Study of Latent Structured Prediction Approaches to Passage Reranking—0
Learning to Extract Folktale Keywords—0
Learning to Focus when Ranking Answers—0
Learning to Order Natural Language Texts—0
Learning to Personalize for Web Search Sessions—0
LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features—0
Learning to Rank Academic Experts in the DBLP Dataset—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles—0
Drug Selection via Joint Push and Learning to Rank—0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems—0
Learning to Rank for Plausible Plausibility—0
Improved Answer Selection with Pre-Trained Word Embeddings—0
Learning to Rank Based on Subsequences—0
Learning to Rank Binary Codes—0
Learning to Rank Broad and Narrow Queries in E-Commerce—0
Learning to Rank by Optimizing NDCG Measure—0
Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision—0
A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints—0
Show:102550
← PrevPage 7 of 16Next →

No leaderboard results yet.