Research on Personalized Learning Service Based on Collaborative Filtering Method

Abstract

In this paper, we discussed various personalized learning recommendation service and their advantages and disadvantages. On the basis of these methods, we proposed the similarity degree computing algorithm and user community discover algorithm. After verifying, analyzing and evaluating these algorithms and the recommendation model, we applied it as a recommendation service in SGCL (Social Group Collaborative Learning) System. Using the model in SGCL system, the system can recommend user personalized information and practical data proves that it can improve the learning quality effectively.