Automatic ranking of information retrieval systems

Maram Hasanain*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Typical information retrieval system evaluation requires expensive manually-collected relevance judgments of documents, which are used to rank retrieval systems. Due to the high cost associated with collecting relevance judgments and the ever-growing scale of data to be searched in practice, ranking of retrieval systems using manual judgments is becoming less feasible. Methods to automatically rank systems in absence of judgments have been proposed to tackle this challenge. However, current techniques are still far from reaching the ranking achieved using manual judgments. I propose to advance research on automatic system ranking using supervised and unsupervised techniques.

Original languageEnglish
Title of host publicationWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages749-750
Number of pages2
ISBN (Electronic)9781450355810
DOIs
Publication statusPublished - 2 Feb 2018
Externally publishedYes
Event11th ACM International Conference on Web Search and Data Mining, WSDM 2018 - Marina Del Rey, United States
Duration: 5 Feb 20189 Feb 2018

Publication series

NameWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
Volume2018-Febuary

Conference

Conference11th ACM International Conference on Web Search and Data Mining, WSDM 2018
Country/TerritoryUnited States
CityMarina Del Rey
Period5/02/189/02/18

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