Fun facts: Automatic trivia fact extraction from wikipedia

David Tsurel, Dan Pelleg, Ido Guy, Dafna Shahaf

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

22 Scopus citations

Abstract

A significant portion of web search queries directly refers to named entities. Search engines explore various ways to improve the user experience for such queries. We suggest augmenting search results with trivia facts about the searched entity. Trivia is widely played throughout the world, and was shown to increase users' engagement and retention. Most random facts are not suitable for the trivia section. There is skill (and art) to curating good trivia. In this paper, we formalize a notion of trivia-worthiness and propose an algorithm that automatically mines trivia facts from Wikipedia. We take advantage ofWikipedia's category structure, and rank an entity's categories by their triviaquality. Our algorithm is capable of finding interesting facts, such as Obama's Grammy or Elvis' stint as a tank gunner. In user studies, our algorithm captures the intuitive notion of "good trivia" 45% higher than prior work. Search-page tests show a 22% decrease in bounce rates and a 12% increase in dwell time, proving our facts hold users' attention.

Original languageAmerican English
Title of host publicationWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages345-354
Number of pages10
ISBN (Electronic)9781450346757
DOIs
StatePublished - 2 Feb 2017
Event10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom
Duration: 6 Feb 201710 Feb 2017

Publication series

NameWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining

Conference

Conference10th ACM International Conference on Web Search and Data Mining, WSDM 2017
Country/TerritoryUnited Kingdom
CityCambridge
Period6/02/1710/02/17

Bibliographical note

Publisher Copyright:
© 2017 ACM.

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