A New Recommendation Approach Based on Implicit Attributes of Learning Material

Abstract A personalized recommendation is an enabling mechanism to overcome information overload occurred in the new learning environments and deliver suitable learner materials to learners. But recommender system technology suffers from some problems such as cold-start and sparsity. Since users express their opinions based on some specific attributes of materials, this paper proposes a new recommender system for learning materials based on their attributes to address these problems. Weight of implicit or latent attributes for learners is considered as chromosomes in genetic algorithm then this algorithm optimizes the weight of implicit attributes for each learner according to historical rating. Then, recommendation is generated using Nearest Neighborhood Algorithm (NNA). The experimental results show that our proposed method outperforms current algorithms and can perform superiorly and alleviates problems such as cold-start and sparsity.