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Iterative Voting under Uncertainty for Group Recommender Systems (Research Abstract)

Research output: Contribution to conference › Paper › peer-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
Pages2400-2401
Number of pages2
DOIs
StatePublished - 2012
Externally publishedYes
Event26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada
Duration: 22 Jul 2012 → 26 Jul 2012

Conference

Conference26th AAAI Conference on Artificial Intelligence, AAAI 2012
Country/TerritoryCanada
CityToronto
Period22/07/12 → 26/07/12

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