Top-N Personalized Recommendation with Graph Neural Networks in MOOCs

Jingjing WANG, Haoran XIE, Fu Lee WANG*, Lap-Kei LEE, Oliver Tat Sheung AU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Top-N personalized recommendation has been extensively studied in assisting learners in finding interesting courses in MOOCs. Although existing Top-N personalized recommendation methods have achieved comparable performance, these models have two major shortcomings. First, these models seldom learn an explicit representation of the structural relation of items. Second, most of these models typically obtain a user’s general preference and neglect the recency of items. This paper proposes a Top-N personalized Recommendation with Graph Neural Network (TP-GNN) in the Massive Open Online Course (MOOCs) as a solution to tackle this problem. We explore two different aggregate functions to deal with the user’s sequence neighbors and then use an attention mechanism to generate the final item representations. The experiments on a real-world course dataset demonstrated that TP-GNN could improve the performances. Furthermore, the system developed based on our method obtains positive feedback from the participants, which denotes that our method effectively predicts learners’ preferences and needs.
Original languageEnglish
Article number100010
JournalComputers & Education: Artificial Intelligence
Volume2
Early online date23 Jan 2021
DOIs
Publication statusPublished - Feb 2021

Bibliographical note

The work described in this paper was supported by the Katie Shu Sui Pui Charitable Trust — Academic Publication Fellowship (KSPF2019-03) of the Open University of Hong Kong, HKIBS Research Seed Fund 2019/20 (190-009) and the Research Seed Fund (102367) of Lingnan University, Hong Kong.

Keywords

  • Recommender Systems
  • MOOCs
  • Graph Neural Networks
  • Personlized Learning

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