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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 401–450 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
Gradient Boosting Neural Networks: GrowNetCode1
Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data—0
Learning to rank for uplift modeling—0
Listwise Learning to Rank with Deep Q-Networks—0
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment—0
Eliminating Search Intent Bias in Learning to Rank—0
JPLink: On Linking Jobs to Vocational Interest Types—0
Boosting API Recommendation with Implicit Feedback—0
Safe Exploration for Optimizing Contextual BanditsCode0
Correcting for Selection Bias in Learning-to-rank Systems—0
Selective Weak Supervision for Neural Information RetrievalCode1
TopRank+: A Refinement of TopRank Algorithm—0
Listwise Learning to Rank by Exploring Unique RatingsCode1
Influence Diagram Bandits—0
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance—0
SetRank: Learning a Permutation-Invariant Ranking Model for Information RetrievalCode0
Duet at TREC 2019 Deep Learning TrackCode0
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity—0
An Alternative Cross Entropy Loss for Learning-to-Rank—0
Separate and Attend in Personal Email Search—0
Policy-Gradient Training of Fair and Unbiased Ranking FunctionsCode0
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment—0
Answering questions by learning to rank - Learning to rank by answering questions—0
ARSM Gradient Estimator for Supervised Learning to Rank—0
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple DocumentsCode0
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