Course recommendation of MOOC with big data support

A contextual online learning approach

Yifan Hou, Pan Zhou, Jie Xu, Dapeng Oliver Wu

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

1 Citation (Scopus)

Abstract

With the advent of the big data era of MOOC, enrolled students and offered courses become numerous and diverse, resulting in a large amount of data and complex curriculum relationships. Thus how to recommend appropriate course to improve students' learning outcomes has become a daunting task. The state-of-the-art works ignore some significant features in course recommendation of MOOC: heterogeneity of large-scale user groups, sequence problem in courses and foreseeable quantitative explosion of courses and users. This paper proposes a systematic methodology for recommending personalized courses with considering the sequence of learning curriculum. The system works by recommending the course with the highest reward to a user. New feedback of the user is then recorded and will be used to improving the performance of recommendation for future students. The core component is a novel online learning algorithm based on hierarchical bandits with known smoothness. We analyze the performance of our proposed online learning algorithm in terms of regret, and prove the asymptotic optimality of the proposed algorithm. Experimental results are provided to verify our theory.

Original languageEnglish (US)
Title of host publicationINFOCOM 2018 - IEEE Conference on Computer Communications Workshops
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages106-111
Number of pages6
ISBN (Electronic)9781538659793
DOIs
StatePublished - Jul 6 2018
Event2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018 - Honolulu, United States
Duration: Apr 15 2018Apr 19 2018

Other

Other2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018
CountryUnited States
CityHonolulu
Period4/15/184/19/18

Fingerprint

Online Learning
Recommendations
Students
Curricula
Learning algorithms
Online Algorithms
Learning Algorithm
Asymptotic Optimality
Explosions
Regret
Student Learning
Reward
Explosion
Feedback
Smoothness
Verify
Big data
Methodology
Experimental Results
Curriculum

Keywords

  • big data
  • course recommendation
  • hierarchical bandits
  • MOOC
  • online learning

ASJC Scopus subject areas

  • Control and Optimization
  • Artificial Intelligence
  • Computer Networks and Communications

Cite this

Hou, Y., Zhou, P., Xu, J., & Wu, D. O. (2018). Course recommendation of MOOC with big data support: A contextual online learning approach. In INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (pp. 106-111). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFCOMW.2018.8406936

Course recommendation of MOOC with big data support : A contextual online learning approach. / Hou, Yifan; Zhou, Pan; Xu, Jie; Wu, Dapeng Oliver.

INFOCOM 2018 - IEEE Conference on Computer Communications Workshops. Institute of Electrical and Electronics Engineers Inc., 2018. p. 106-111.

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

Hou, Y, Zhou, P, Xu, J & Wu, DO 2018, Course recommendation of MOOC with big data support: A contextual online learning approach. in INFOCOM 2018 - IEEE Conference on Computer Communications Workshops. Institute of Electrical and Electronics Engineers Inc., pp. 106-111, 2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018, Honolulu, United States, 4/15/18. https://doi.org/10.1109/INFCOMW.2018.8406936
Hou Y, Zhou P, Xu J, Wu DO. Course recommendation of MOOC with big data support: A contextual online learning approach. In INFOCOM 2018 - IEEE Conference on Computer Communications Workshops. Institute of Electrical and Electronics Engineers Inc. 2018. p. 106-111 https://doi.org/10.1109/INFCOMW.2018.8406936
Hou, Yifan ; Zhou, Pan ; Xu, Jie ; Wu, Dapeng Oliver. / Course recommendation of MOOC with big data support : A contextual online learning approach. INFOCOM 2018 - IEEE Conference on Computer Communications Workshops. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 106-111
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