The study of personalized recommendation technology based content and project collaborative filtering combines

Collaborative filtering is more successful techniques which in personalized recommendation system. However, with the site structure, content of the complexity and increasing number of users, collaborative filtering algorithm has encountered real-time, data sparseness; scalability and cold start other problems. In view of this deficiency, this paper is proposed combination recommendation technologies to improve collaborative filtering algorithms, and for the improved algorithm to simulation experiments, verify the improved algorithm is reasonable and effective, Effective improve the recommendation quality of electronic commerce recommendation algorithm.