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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 401–425 of 753 papers

TitleStatusHype
Learning to rank music tracks using triplet loss—0
Policy-Aware Unbiased Learning to Rank for Top-k RankingsCode0
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction—0
That is a Known Lie: Detecting Previously Fact-Checked ClaimsCode1
Modeling Document Interactions for Learning to Rank with Regularized Self-Attention—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
RaCT: Toward Amortized Ranking-Critical Training For Collaborative FilteringCode1
Learning to Rank Intents in Voice Assistants—0
Query-level Early Exit for Additive Learning-to-Rank Ensembles—0
Valid Explanations for Learning to Rank Models—0
Unbiased Learning to Rank: Online or Offline?—0
Fast and Memory-Efficient Neural Code Completion—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions—0
Learning-to-Rank with BERT in TF-Ranking—0
Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph—0
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection—0
Hierarchical Entity Typing via Multi-level Learning to RankCode1
A Recurrent Model for Collective Entity Linking with Adaptive FeaturesCode0
Towards Productionizing Subjective Search Systems—0
TREC CAsT 2019: The Conversational Assistance Track OverviewCode1
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online—0
Identifying Notable News Stories—0
StochasticRank: Global Optimization of Scale-Free Discrete Functions—0
Handling Position Bias for Unbiased Learning to Rank in Hotels Search—0
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