Adaptive Preference Learning With Bandit Feedback: Information Filtering, Dueling Bandits and Incentivizing Exploration

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.

Name thesis:degree_name

Ph. D., Operations Research

Level thesis:degree_level

Doctor of Philosophy

Discipline thesis:degree_discipline

Operations Research

Grantor

Cornell University

Year dc:date.issued

2017

Author dc:creator

• Chen, Bangrui

Committee members dc:contributor.committeemember

• Topaloglu, Huseyin
• Joachims, Thorsten

• Statistics
• Operations research
• Computer science
• adaptive preference learning
• bandit feedback
• dueling bandits
• incentivizing exploration
• information filtering
• multi-armed bandits

Language dc:language.iso

en_US

DOI

https://doi.org/10.7298/X4251GCQ

Dc Identifier Other

ProQuest Submission ID: 10605
ProQuest Publication ID: 10680541

OAI identifier oai:identifier

oai:ecommons.cornell.edu:1813/59050