Attribute-based recommender system for learning resource by learner preference tree

In recent years, with growth of online learning technology, a huge amount of e-learning resources have been generated in various media formats. This growth has caused difficulty of locating appropriate learning resources to learners. A personalized recommendation is an enabling mechanism to overcome information overload occurred in the new learning environments and deliver suitable learner resources to learners. Since users express their opinions based on some specific attributes of items, this paper considers contextual information including attributes of learning resources and rating of learner simultaneously to address some problem such as sparsity and cold start problem and also improve the quality on recommendations. Learning Tree (LT) is introduced that can model the interest of learners based on attributes of learning resources in multidimensional space using learner historical accessed resources. Then, using a new similarity measure between learners, recommendations are generated. The experimental results show that our proposed method outperforms current algorithms and alleviates problems such as cold-start and sparsity.