Collaborative Filtering Recommendation Algorithm Classification and Comparative Study

Abstract

With the rapid development of the information society, both the producers of teaching resources and students are facing enormous challenges. In this context, a recommendation system that can connect the producers and students of teaching resources to achieve a win-win situation is proposed. Collaborative filtering is currently the most successful recommendation technique. In this article the authors firstly introduced the collaborative filtering recommendation algorithm from the angle of user-based, project-based and model-based algorithms. Then they studied the current popular SVD and RBM recommendation algorithms mainly by comparing the experimental results of the basic project-based, SVD and RBM algorithms on the public data set movielens, and pointed out that the recommendation system will be a real-time, multi-modal trend in the age of educational informatization. The rapid development of education informatization has made MOOC, micro-classes and other teaching resources mainly relying on video resources to grow rapidly. The collaborative filtering recommendation algorithm expects to associate the most suitable high-quality resources with the most needed students.