Multi-stakeholder Personalized Learning with Preference Corrections
Yong Wei Zheng · 2019
Recommender systems (RS) have served as an effective technology-enhanced learning technique in the learning area. Traditional RS only consider the preferences of the end user who is the receiver the recommendations. Multi-stakeholder recommender systems (MSRS) are recently proposed to balance the needs of multiple stakeholders in the recommender systems. For example, the utility of the learning materials from the perspective of parents, instructors and even publishers may be also important in addition to the students' preferences in the area of educational learning. In this paper, we propose and exploit utility-based MSRS for personalized learning. Particularly, we develop our methods for preference corrections, in order to address the issue that instructors and students may have different perceptions on multiple aspects of the course projects. Our experimental results based on an educational data demonstrate the effectiveness of our proposed solutions.