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

Lihi Dery

doi:10.1609/aaai.v26i1.8185

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

Lihi Dery

Year: 2021 Journal: Proceedings of the AAAI Conference on Artificial Intelligence Vol: 26 (1)Pages: 2400-2401 Publisher: Association for the Advancement of Artificial Intelligence

DOI: 10.1609/aaai.v26i1.8185

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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

Keywords:

Voting Recommender system Probabilistic logic Computer science Group (periodic table) Order (exchange) Mathematical optimization Theoretical computer science Artificial intelligence Machine learning Mathematics Political science Economics

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Recommender Systems and Techniques

Physical Sciences → Computer Science → Information Systems

Advanced Bandit Algorithms Research

Social Sciences → Decision Sciences → Management Science and Operations Research

Mobile Crowdsensing and Crowdsourcing

Physical Sciences → Computer Science → Computer Science Applications

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

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