Design and Implementation of Personalized MOOC Recommendation System Based on Spark
Donghui Li, Xi Zhang, Xinyu Luo · 2022
With the popularity of MOOCs, the number of MOOC platforms and courses has increased rapidly. However, in the face of massive and scattered course information, learners usually need to access different platforms, search for related topics, read course introductions and course syllabuses, and finally, choose one course that suits them. Learners are now facing Serious problems of information trek and information overload. Therefore, aggregating course information from various platforms and using machine learning technology to provide learners with accurate course recommendations can greatly improve the learning efficiency and enthusiasm of learners. This paper first builds a distributed course and user data collection tool. Basing on the collected the collected course and user information, the course attributes and user learning behavior are modeled. Using generated knowledge about users and courses, a course recommender system is designed, which is based on Spark framework and can provide users with scientific and personalized course recommendations.