A New Recommender Model of Collaborative Filtering Based on User

Nowadays, Web has become the main way to gain information. However, "Information overload" and "information lack" has become a big problem to be studied. To provide the personalized service for people is especially essential. However, existing collaborative filtering algorithms have been suffering from data sparsity and scalability problems which lead to inaccuracy of recommendation. In this paper, a recommendation model of collaborative filtering based on user is proposed. The results of experiment show that the model can improve the two problems that traditional collaborative filtering faced efficiently. Simultaneously the quality of information recommendation also has the distinct enhancement compares to the traditional recommendation.