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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 351–400 of 753 papers

TitleStatusHype
De-Biased Modelling of Search Click Behavior with Reinforcement Learning—0
RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search—0
Scalable Personalised Item Ranking through Parametric Density Estimation—0
Federated Unbiased Learning to Rank—0
Ranking Structured Objects with Graph Neural NetworksCode0
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context—0
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search—0
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading—0
Community-based Cyberreading for Information Understanding—0
Fairness in Ranking: A Survey—0
Attention-based neural re-ranking approach for next city in trip recommendations—0
Individually Fair Ranking—0
PairRank: Online Pairwise Learning to Rank by Divide-and-ConquerCode0
Neural Feature Selection for Learning to Rank—0
Maximizing Marginal Fairness for Dynamic Learning to RankCode0
Information Ranking Using Optimum-Path Forest—0
Leveraging User Behavior History for Personalized Email Search—0
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to RankCode0
Fairness Through Regularization for Learning to Rank—0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—0
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval—0
Analysis of E-commerce Ranking Signals via Signal Temporal Logic—0
Metric Learning for Session-based RecommendationsCode0
Individually Fair Rankings—0
Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees—0
Few-Shot Text Ranking with Meta Adapted Synthetic Weak SupervisionCode0
A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces—0
Autoregressive Reasoning over Chains of Facts with TransformersCode0
Building Cross-Sectional Systematic Strategies By Learning to Rank—0
Learning from User Interactions with Rankings: A Unification of the Field—0
From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews—0
MatRec: Matrix Factorization for Highly Skewed Dataset—0
Extended Missing Data Imputation via GANs for Ranking Applications—0
U-rank: Utility-oriented Learning to Rank with Implicit Feedback—0
What Are You Trying to Do? Semantic Typing of Event Processes—0
Embedding Meta-Textual Information for Improved Learning to Rank—0
Addressing Purchase-Impression Gap through a Sequential Re-ranker—0
Self-Supervised Ranking for Representation Learning—0
"What Are You Trying to Do?" Semantic Typing of Event Processes—0
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to RankCode0
Refining Data for Text Generation—0
On the Problem of Underranking in Group-Fair RankingCode0
Learning to Personalize for Web Search Sessions—0
Time-Aware Evidence Ranking for Fact-Checking—0
Learning to Rank under Multinomial Logit Choice—0
Optimize What You Evaluate With: A Simple Yet Effective Framework For Direct Optimization Of IR Metrics—0
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to RankCode0
Sample-Rank: Weak Multi-Objective Recommendations Using Rejection Sampling—0
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to RankCode0
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation—0
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