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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 626–650 of 753 papers

TitleStatusHype
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
WMRB: Learning to Rank in a Scalable Batch Training Approach—0
Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering—0
Beyond Pairwise Learning-To-Rank At Airbnb—0
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online—0
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
Biomedical Document Retrieval for Clinical Decision Support System—0
Block-distributed Gradient Boosted Trees—0
Boosting API Recommendation with Implicit Feedback—0
Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings—0
Improving Neural Ranking via Lossless Knowledge Distillation—0
Bounded-Abstention Pairwise Learning to Rank—0
Scale-Invariant Learning-to-Rank—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
Bring you to the past: Automatic Generation of Topically Relevant Event Chronicles—0
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback—0
Building Cross-Sectional Systematic Strategies By Learning to Rank—0
Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank—0
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training—0
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank—0
Cascading Bandits: Learning to Rank in the Cascade Model—0
Cascading Bandits Robust to Adversarial Corruptions—0
Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model—0
Challenges in clinical natural language processing for automated disorder normalization—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015—0
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