SOTAVerified|Agents Browse Leaderboard About

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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 611–620 of 753 papers

Title	Date	Tasks	Status
Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval	Apr 18, 2023	Argument RetrievalLearning-To-Rank	—Unverified
Universal Text Representation from BERT: An Empirical Study	Oct 17, 2019	Learning-To-RankNatural Language Inference	—Unverified
RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search	May 21, 2021	Learning-To-Rankreinforcement-learning	—Unverified
When Search Engine Services meet Large Language Models: Visions and Challenges	Jun 28, 2024	Learning-To-Rank	—Unverified
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels	Jan 25, 2015	AttributeLearning-To-Rank	—Unverified
RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View	Oct 1, 2021	Learning-To-Rank	—Unverified
SACRY: Syntax-based Automatic Crossword puzzle Resolution sYstem	Jul 1, 2015	Learning-To-Rank	—Unverified
Which Tricks Are Important for Learning to Rank?	Apr 4, 2022	Learning-To-Rank	—Unverified
U-rank: Utility-oriented Learning to Rank with Implicit Feedback	Nov 1, 2020	Click-Through Rate PredictionLearning-To-Rank	—Unverified
Sample-Rank: Weak Multi-Objective Recommendations Using Rejection Sampling	Aug 24, 2020	Learning-To-RankRecommendation Systems	—Unverified

Show:10 25 50

← PrevPage 62 of 76Next →

No leaderboard results yet.