New Recommender System for Online Courses Using Knowledge Graph Modeling

Nowadays, customers can get recommendations for products and services that fit their anticipated needs in the context of a large body of data. Advances in this area provide novel concepts and enable technology for use in e-learning settings. The existing recommendation systems typically focus on improving the item side, but they don’t consider the user characteristics in the recommendation. Thus, they’re ineffective and hence not a good fit for a virtual classroom setting. This research focuses on applying semantics-based knowledge graph modeling with collaborative filtering (CF) to solve these issues. This proposed framework applies the users’ static features to aid in cold start issues for potential new users. The proposed framework significantly outperforms the existing models and helps maintain acceptable performance using better user-item interactions.