Research Area:  Machine Learning
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 users 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 users 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.
Author(s) Name:  Jingjing Wang,Haoran Xie,Fu Lee Wang,Lap-Kei Lee,Oliver Tat Sheung Au
Journal name:  Computers and Education: Artificial Intelligence
Publisher name:  Elsevier
Volume Information:  Volume 2, 2021, 100010
Paper Link:   https://www.sciencedirect.com/science/article/pii/S2666920X21000047