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dc.contributor.authorChen, Bangrui
dc.date.accessioned2018-10-03T19:27:23Z
dc.date.available2018-10-03T19:27:23Z
dc.date.issued2017-12-30
dc.identifier.otherChen_cornellgrad_0058F_10605
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:10605
dc.identifier.otherbibid: 10474153
dc.identifier.urihttps://hdl.handle.net/1813/59050
dc.description.abstractIn this thesis, we study adaptive preference learning, in which a machine learning system learns users' preferences from feedback while simultaneously using these learned preferences to help them find preferred items. We study three different types of user feedback in three application setting: cardinal feedback with application in information filtering systems, ordinal feedback with application in personalized content recommender systems, and attribute feedback with application in review aggregators. We connect these settings respectively to existing work on classical multi-armed bandits, dueling bandits, and incentivizing exploration. For each type of feedback and application setting, we provide an algorithm and a theoretical analysis bounding its regret. We demonstrate through numerical experiments that our algorithms outperform existing benchmarks.
dc.language.isoen_US
dc.subjectStatistics
dc.subjectOperations research
dc.subjectComputer science
dc.subjectadaptive preference learning
dc.subjectbandit feedback
dc.subjectdueling bandits
dc.subjectincentivizing exploration
dc.subjectinformation filtering
dc.subjectmulti-armed bandits
dc.titleAdaptive Preference Learning With Bandit Feedback: Information Filtering, Dueling Bandits and Incentivizing Exploration
dc.typedissertation or thesis
thesis.degree.disciplineOperations Research
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Operations Research
dc.contributor.chairFrazier, Peter
dc.contributor.committeeMemberTopaloglu, Huseyin
dc.contributor.committeeMemberJoachims, Thorsten
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/X4251GCQ


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