How to build high quality L2R training data: Unsupervised compression-based selective sampling for learning to rank
- 7 April 2022
- journal article
- research article
- Published by Elsevier in Information Sciences
- Vol. 601, 90-113
Abstract
No abstract availableKeywords
Funding Information
- 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
This publication has 13 references indexed in Scilit:
- A Two‐stage active learning method for learning to rankJournal of the Association for Information Science and Technology, 2013
- About learning models with multiple query-dependent featuresACM Transactions on Information Systems, 2013
- Flexible sample selection strategies for transfer learning in rankingInformation Processing & Management, 2011
- LETOR: A benchmark collection for research on learning to rank for information retrievalInformation Retrieval Journal, 2010
- Learning to Rank for Information RetrievalFoundations and Trends® in Information Retrieval, 2009
- Transitive indistinguishability and approximate measurement with standard finite ratio-scale representationsJournal of Mathematical Psychology, 2006
- A study of smoothing methods for language models applied to information retrievalACM Transactions on Information Systems, 2004
- Large test collection experiments on an operational, interactive system: Okapi at TRECInformation Processing & Management, 1995
- An optimal convex hull algorithm in any fixed dimensionDiscrete & Computational Geometry, 1993
- A Mathematical Theory of CommunicationBell System Technical Journal, 1948