Iterative voting under uncertainty for group recommender systems (Research abstract)

Lihi Naamani-Dery

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

Abstract

Group Recommendation Systems (GRS's) assist groups when trying to reach a joint decision. I use probabilistic data and apply voting theory to GRS's in order to minimize user interaction and output an approximate or definite "winner item".

Original languageEnglish
Title of host publicationAAAI-12 / IAAI-12 - Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference
Pages2400-2401
Number of pages2
StatePublished - 7 Nov 2012
Event26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 - Toronto, ON, Canada
Duration: 22 Jul 201226 Jul 2012

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume3

Conference

Conference26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
Country/TerritoryCanada
CityToronto, ON
Period22/07/1226/07/12

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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