How to build high quality L2R training data: Unsupervised compression-based selective sampling for learning to rank-Reference-Cited by-同舟云学术

How to build high quality L2R training data: Unsupervised compression-based selective sampling for learning to rank

Published:2022-07 Issue: Volume:601 Page:90-113
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Silva Rodrigo M.,Gomes Guilherme C.M.,Alvim Mário S.,Gonçalves Marcos A.

Funder

Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference43 articles.

1. Introduction to Linear Optimization;Bertsimas,1997

2. From RankNet to LambdaRank to LambdaMART: An Overview. Technical Report MSR-TR-2010-82;Burges,2010

3. Relevant knowledge helps in choosing right teacher: Active query selection for ranking adaptation;Cai,2011

4. Variance maximization via noise injection for active sampling in learning to rank;Cai,2012

5. Active Learning for Classification with Maximum Model Change;Cai;ACM Trans. Inf. Syst.,2017

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Self-Distilled Learning to Rank Model for Ad-hoc Retrieval;ACM Transactions on Information Systems;2024-07-25

2. Tracking and handling behavioral biases in active learning frameworks;Information Sciences;2023-09

3. Reducing the user labeling effort in effective high recall tasks by fine-tuning active learning;Journal of Intelligent Information Systems;2023-01-19