E-learning Recommendation System

E-learning recommendation system helps learners to make choices without sufficient personal experience of the alternatives, and it is considerably requisite in this information explosion age. In our study, the user-based collaborative filtering method is chosen as the primary recommendation algorithm, combined with online education. We analyze the requirement of a web-based e-learning recommendation system, and divide the system workflow into five sections: data collection, data ETL, model generation, strategy configuration, and service supply. Moreover, an architecture is proposed, based on which further development can be accomplished. In this architecture, there are seven modules, and four of them are core modules: recommendation models database, recommendation system database, recommendation management, data/model management.