Kernelized cost-sensitive listwise ranking
Loading...
Date
2018
Authors
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky
Altmetrics
Abstract
Learning to Rank is an area of application in machine learning, typically supervised, to build ranking models for Information Retrieval systems. The training data consists of lists of items with some partial order specified induced by an ordinal or binary score. The model purpose is to produce a permutation of the items in this list in a way which is close to the rankings in the training data. This technique has been successfully applied to ranking, and several approaches have been proposed since then, including the Listwise approach. A cost-sensitive version of that is an adaptation of this framework which treats the documents within a list with different probabilities, i.e., attempt to impose weights for the documents with higher cost. We then take this algorithm to the next level by kernelizing the loss and exploring the optimization in different spaces. Among the different existing likelihood algorithms, we choose ListMLE as pri- mary focus of experimentation, since it has been shown to be the approach with the best empirical performance. The theoretical framework is given along with its mathematical properties.
Description
Citation
Mathematics for Applications. 2018 vol. 7, č. 1, s. 31-40. ISSN 1805-3629
http://ma.fme.vutbr.cz/archiv/7_1/ma_7_1_3_olinto_fokoue_final.pdf
http://ma.fme.vutbr.cz/archiv/7_1/ma_7_1_3_olinto_fokoue_final.pdf
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
© Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky