Adaptive Preference Learning With Bandit Feedback: Information Filtering, Dueling Bandits and Incentivizing Exploration
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In 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.
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2017-12-30
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Statistics; Operations research; Computer science; adaptive preference learning; bandit feedback; dueling bandits; incentivizing exploration; information filtering; multi-armed bandits
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Frazier, Peter
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Topaloglu, Huseyin
Joachims, Thorsten
Joachims, Thorsten
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Operations Research
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Ph. D., Operations Research
Degree Level
Doctor of Philosophy
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dissertation or thesis