The Enhancement and Application of Collaborative Filtering in e-Learning System

Collaborative Filtering recommendation algorithm is one of the most popular approaches for determining recommendations at present and it can be used to solve Information Overload issue in e-Learning system. However the Cold Start problem is always one of the most critical issues that affect the performance of Collaborative Filtering recommender system. In this paper an enhanced composite recommendation algorithm based on content recommendation tags extracting and CF is proposed to make the CF recommender system work more effectively. The final experiment results show that the new enhanced recommendation algorithm has some advantages on accuracy compared with several existing solutions to the issue of Cold Start and make sure that it is a feasible and effective recommendation algorithm.