(untitled) (International Conference on Computers in Education, 2019)
Recommender systems have been applied as one of the technology-enhanced learning techniques for educations. Recently, multi-stakeholder recommender systems were proposed to balance the needs among different stakeholders, especially when there are conflicts of interests. In this paper, we create student communities by using the clustering technique, and seek the impact of these communities in the educational recommendations. Our experimental results identify the best community which can improve the multi-stakeholder recommendations.