Knowledge graph-assisted collaborative filtering for course recommendation in Mooc

Abstract

Due to the Internet's tremendous expansion in recent years, quite a few students have started studying on massive open online course platforms. The information explosion of online education platforms makes it challenging for users to choose their courses effectively. The course recommendation system has become the most effective way for online education platforms to solve the above problems. One of the classic algorithms used in recommendation algorithms is collaborative filtering, but it also has many limitations: 1) Collaborative filtering is affected by cold start and sparsity easily; 2) Collaborative filtering needs to be used for rating information on courses, but the real user data in the online education platform basically does have user rating information on courses nearly, so it is challenging to apply collaborative filtering in the field of course recommendation. So, we propose an algorithm and define a formula to calculate the user's degree of interest (rating) to the course based on the user's interaction information in this paper. It adopts the knowledge graph embedding representation learning method to embed the semantic information of the course into the low-dimensional semantic space, and then use the similarity calculation formula to get the acquaintance between users according to the embedded expression and perform collaborative filtering calculation. Our method is compared with user-based collaborative filtering and item-based collaborative filtering and the model is effective on real data, according to experimental results.