Axiomatic analysis and optimization of information retrieval models

Hui Fang, Chengxiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Axiomatic approach provides a systematic way to think about heuristics, identify the weakness of existing methods, and optimize the existing methods accordingly. This tutorial aims to promote axiomatic thinking that can benefit not only the study of IR models but also the methods for many IR applications.

Original languageEnglish (US)
Title of host publicationSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Number of pages1
ISBN (Print)9781450322591
DOIs
StatePublished - Jan 1 2014
Event37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 - Gold Coast, QLD, Australia
Duration: Jul 6 2014Jul 11 2014

Publication series

NameSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014
CountryAustralia
CityGold Coast, QLD
Period7/6/147/11/14

Keywords

  • Axiomatic analysis
  • Information retrieval models
  • Optimization
  • Retrieval constraints

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems

Fingerprint Dive into the research topics of 'Axiomatic analysis and optimization of information retrieval models'. Together they form a unique fingerprint.

  • Cite this

    Fang, H., & Zhai, C. (2014). Axiomatic analysis and optimization of information retrieval models. In SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery. https://doi.org/10.1145/2600428.2611178