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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 601–650 of 753 papers

TitleStatusHype
Reinforcement Learning to Rank with Coarse-grained Labels—0
Universalizing Weak Supervision—0
RelationListwise for Query-Focused Multi-Document Summarization—0
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank—0
Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain—0
Reqo: A Robust and Explainable Query Optimization Cost Model—0
Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition—0
Resolving Entity Morphs in Censored Data—0
Responding E-commerce Product Questions via Exploiting QA Collections and Reviews—0
Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs—0
Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval—0
Universal Text Representation from BERT: An Empirical Study—0
RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search—0
When Search Engine Services meet Large Language Models: Visions and Challenges—0
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels—0
RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View—0
SACRY: Syntax-based Automatic Crossword puzzle Resolution sYstem—0
Which Tricks Are Important for Learning to Rank?—0
U-rank: Utility-oriented Learning to Rank with Implicit Feedback—0
Sample-Rank: Weak Multi-Objective Recommendations Using Rejection Sampling—0
Whole Page Unbiased Learning to Rank—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Scalable Personalised Item Ranking through Parametric Density Estimation—0
Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments—0
BanditRank: Learning to Rank Using Contextual Bandits—0
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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