Recommendation Systems for Online Lectures Based on Collaborative Filtering and Content Based Filtering

Abstract

In this paper, we compare four typical recommendation algorithms aiming at online lecture recommendation: collaborative filtering, matrix factorization, tf-idf, and word2vec. The experimental results using 1,958 videos in Schoo showed that the matrix factorization is the best (precision was 0.28@10), followed by collaborative filtering. On the other hand, content-based filtering did not work well for lecture recommendation.