Personalized Educational Learning with Multi-Stakeholder Optimizations

Recommender systems (RS) have been introduced to educations as an effective technology-enhanced learning technique. Traditional RS produce recommendations by considering the preferences of the end users only. Multi-stakeholder recommender systems (MSRS) claim that it is necessary to consider the utility of the items from the perspective of other stakeholders in order to balance the needs of multiple stakeholders. Take book recommendations for example, the utility of items from the view of parents, instructors and even publishers may be also important in addition to the student preferences. In this paper, we propose and exploit utility-based MSRS for personalized learning. Particularly, we attempt to address the challenge of over-/under-expectations in the utility-based MSRS. Our experimental results based on an educational data demonstrate the effectiveness of our proposed models and solutions.