SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 71–80 of 753 papers

TitleStatusHype
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Multi-objective Learning to Rank by Model Distillation—0
Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering PlatformsCode0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
When Search Engine Services meet Large Language Models: Visions and Challenges—0
Learning to Rank for Maps at Airbnb—0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback—0
FIRST: Faster Improved Listwise Reranking with Single Token DecodingCode2
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language ModelsCode2
Step-level Value Preference Optimization for Mathematical ReasoningCode3
Show:102550
← PrevPage 8 of 76Next →

No leaderboard results yet.