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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
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers—0
Two-Layer Generalization Analysis for Ranking Using Rademacher Average—0
ULTRA: An Unbiased Learning To Rank Algorithm Toolbox—0
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation—0
Unbiased Learning to Rank: Counterfactual and Online Approaches—0
Unbiased Learning to Rank: Online or Offline?—0
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction—0
Unbiased Learning-to-Rank with Biased Feedback—0
Unbiased Learning to Rank with Biased Continuous Feedback—0
Unbiased Offline Evaluation for Learning to Rank with Business Rules—0
Uncertain Natural Language Inference—0
Unconfounded Propensity Estimation for Unbiased Ranking—0
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality—0
Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models—0
Understanding the Gist of Images - Ranking of Concepts for Multimedia Indexing—0
Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank—0
Universalizing Weak Supervision—0
Universal Text Representation from BERT: An Empirical Study—0
U-rank: Utility-oriented Learning to Rank with Implicit Feedback—0
Using Learning-To-Rank to Enhance NLM Medical Text Indexer Results—0
Valid Explanations for Learning to Rank Models—0
Variance Reduction in Gradient Exploration for Online Learning to Rank—0
Visualization on Financial Terms via Risk Ranking from Financial Reports—0
ViTOR: Learning to Rank Webpages Based on Visual Features—0
VSoLSCSum: Building a Vietnamese Sentence-Comment Dataset for Social Context Summarization—0
Weakly-supervised Contextualization of Knowledge Graph Facts—0
Weak Supervision for Improved Precision in Search Systems—0
Web-Scale Responsive Visual Search at Bing—0
"What Are You Trying to Do?" Semantic Typing of Event Processes—0
What Are You Trying to Do? Semantic Typing of Event Processes—0
What makes you change your mind? An empirical investigation in online group decision-making conversations—0
When Search Engine Services meet Large Language Models: Visions and Challenges—0
Which Tricks Are Important for Learning to Rank?—0
Whole Page Unbiased Learning to Rank—0
WMRB: Learning to Rank in a Scalable Batch Training Approach—0
Word-Entity Duet Representations for Document Ranking—0
Zeroshot Listwise Learning to Rank Algorithm for Recommendation—0
Joint Upper & Lower Bound Normalization for IR Evaluation—0
JPLink: On Linking Jobs to Vocational Interest Types—0
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions—0
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models—0
Language Modelling via Learning to Rank—0
Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces—0
Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph—0
LDTM: A Latent Document Type Model for Cumulative Citation Recommendation—0
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System—0
Learning diverse rankings with multi-armed bandits—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Learning Efficient Anomaly Detectors from K-NN Graphs—0
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages—0
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