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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 601–625 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
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