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.
|Journal||Computers & Education: Artificial Intelligence|
|Early online date||23 Jan 2021|
|Publication status||Published - Feb 2021|
Bibliographical noteFunding Information:
The work described in this paper was supported by the Katie Shu Sui Pui Charitable Trust — Academic Publication Fellowship (Project Reference No. KSPF2019-03 ) of the Open University of Hong Kong , Hong Kong, HKIBS Research Seed Fund 2019/20 (190-009) and the Research Seed Fund ( 10236 7 ) of Lingnan University, Hong Kong .
© 2021 The Authors
- Recommender Systems
- Graph Neural Networks
- Personlized Learning