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OfficeHours: A System for Student Supervisor Matching through Reinforcement Learning

Published: 29 March 2015 Publication History

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.

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References

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Cited By

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  • (2020)Thesis Supervisor Recommendation with Representative Content and Information RetrievalJournal of Information Systems Engineering and Business Intelligence10.20473/jisebi.6.2.143-1506:2(143)Online publication date: 27-Oct-2020
  • (2020)Introduction to Bandits in Recommender SystemsProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3411547(748-750)Online publication date: 22-Sep-2020
  • (2019)Bandit algorithms in recommender systemsProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346956(574-575)Online publication date: 10-Sep-2019
  • Show More Cited By

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  1. OfficeHours: A System for Student Supervisor Matching through Reinforcement Learning

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    cover image ACM Conferences
    IUI '15 Companion: Companion Proceedings of the 20th International Conference on Intelligent User Interfaces
    March 2015
    164 pages
    ISBN:9781450333085
    DOI:10.1145/2732158
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 29 March 2015

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    Author Tags

    1. exploratory search
    2. student support systems

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    • Academy of Finland
    • TEKES

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    IUI '15 Companion Paper Acceptance Rate 47 of 205 submissions, 23%;
    Overall Acceptance Rate 746 of 2,811 submissions, 27%

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    View all
    • (2020)Thesis Supervisor Recommendation with Representative Content and Information RetrievalJournal of Information Systems Engineering and Business Intelligence10.20473/jisebi.6.2.143-1506:2(143)Online publication date: 27-Oct-2020
    • (2020)Introduction to Bandits in Recommender SystemsProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3411547(748-750)Online publication date: 22-Sep-2020
    • (2019)Bandit algorithms in recommender systemsProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346956(574-575)Online publication date: 10-Sep-2019
    • (2017)Bandit Algorithms in Interactive Information RetrievalProceedings of the ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3121050.3121108(327-328)Online publication date: 1-Oct-2017

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