Collaborative Filtering Recommendation Algorithm Classification and Comparative Study
Duan Chao, Kaili Lu, Jing Zhang, Jerry Xie · 2019
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.