A temporally adaptive content-based relevance ranking algorithm

Jukka Perkiö, Wray Buntine, Henry Tirri

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

8 Citations (Scopus)

Abstract

In information retrieval relevance ranking of the results is one of the most important single tasks there are. There are many diffierent ranking algorithms based on the content of the documents or on some external properties e.g. link structure of html documents.We present a temporally adaptive content-based relevance ranking algorithm that explicitly takes into account the temporal behavior of the underlying statistical properties of the documents in the form of a statistical topic model. more we state that our algorithm can be used on top of any ranking algorithm.

Original languageEnglish
Title of host publicationSIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery (ACM)
Pages647-648
Number of pages2
ISBN (Print)1595930345, 9781595930347
DOIs
Publication statusPublished - 1 Dec 2005
Externally publishedYes
EventACM International Conference on Research and Development in Information Retrieval 2005 - Salvador, Brazil
Duration: 15 Aug 200519 Aug 2005
Conference number: 28th
https://dl.acm.org/doi/proceedings/10.1145/1076034

Publication series

NameSIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

ConferenceACM International Conference on Research and Development in Information Retrieval 2005
Abbreviated titleSIGIR 2005
CountryBrazil
CitySalvador
Period15/08/0519/08/05
Internet address

Keywords

  • MPCA
  • relvance ranking
  • temporal adaptation
  • tf-idf
  • topic model

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