A fuzzy tree matching-based personalised e-learning recommender system

The rapid development of e-learning systems provides learners great opportunities to access the learning activities online, which greatly supports and enhances learning practices. However, too many learning activities are emerging in the e-learning system, which makes it difficult for learners to select proper ones for their particular situations since there is no personalised service function. Recommender systems, which aim to provide personalised recommendations, can be used to solve this issue. However, e-learning systems have two features to handle: (1) data of learners and leaning activities often present tree structures; (2) data are often vague and uncertain in practice. In this study, a fuzzy tree-structured data model is proposed to comprehensively describe the complex learning activities and learner profiles. A tree matching method is then developed to match the similar learning activities or learners. To deal with the uncertain category issues, a fuzzy category tree and relevant similarity measure are developed. A hybrid recommendation approach, which considers precedence relations between learning activities and combines the semantic and collaborative filtering similarities between learners, is developed. The proposed approach can handle the special requirements in e-learning environment and make proper recommendations in e-learning systems.