• Media type: E-Book
  • Title: How important are user-generated data for search result quality? Experimental evidence
  • Contributor: Klein, Tobias J. [VerfasserIn]; Kurmangaliyeva, Madina [VerfasserIn]; Prüfer, Jens [VerfasserIn]; Prüfer, Patricia [VerfasserIn]
  • imprint: [S.l.]: SSRN, 2022
  • Published in: TILEC Discussion Paper ; No. 2022-016
  • Extent: 1 Online-Ressource (40 p)
  • Language: English
  • DOI: 10.2139/ssrn.4229292
  • Identifier:
  • Keywords: Search engine quality ; algorithm ; user-generated data ; data-driven markets ; experiment ; Arbeitspapier ; Graue Literatur
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 22, 2022 erstellt
  • Description: Do some search engines produce better search results because their algorithm is better, or because they have access to more data from past searches? In the latter case, mandatory data sharing, a policy that is currently discussed, could trigger innovation and would benefit all users of search engines. We document that the algorithm of a small search engine can produce non-personalized results that are of similar quality than Google’s, if it has enough data, and that overall differences in the quality of search results are explained by searches for less popular search terms. This is confirmed by results from an experiment, in which we keep the algorithm of the search engine fixed and vary the amount of data it uses as an input
  • Access State: Open Access