OfficeHours: A System for Student Supervisor Matching Through Reinforcement Learning

Yuan Gao, Kalle Ilves, Dorota Glowacka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe OfficeHours, a recommender system that assists students in finding potential supervisors for their dissertation projects. OfficeHours is an interactive recommender system that combines reinforcement learning techniques with a novel interface that assists the student in formulating their query and allows active engagement in directing their search. Students can directly manipulate document features (keywords) extracted from scientific articles written by faculty members to indicate their interests and reinforcement learning is used to model the student's interests by allowing the system to trade off between exploration and exploitation. The goal of system is to give the student the opportunity to more effectively search for possible project supervisors in a situation where the student may have difficulties formulating their query or when very little information may be available on faculty members' websites about their research interests.
Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Intelligent User Interfaces Companion
Place of PublicationNew York, NY, USA
PublisherACM
Pages29-32
Number of pages4
ISBN (Print)978-1-4503-3308-5
DOIs
Publication statusPublished - 2015

Publication series

NameIUI Companion '15
PublisherACM

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