An improved similarity measure method in Collaborative Filtering Recommendation Algorithm

Collaborative filtering recommendation technology is successfully used in personalized recommendation services. Since the magnitudes of users and commodities in E-commerce system has increased dramatically, the user rating data in the entire item space become extremely sparse. There is a certain deviation while using traditional similarity measure methods, which reduces the recommendation accuracy for the recommendation systems. To overcome the shortages of the traditional similarity measures under such conditions, this paper proposes using similarity impact factor to improve similarity measures in collaborative filtering recommendation algorithms. The experimental results show that the factor can effectively improve the similarity measure result while user rating data are extremely sparse, and significantly improve the accuracy of the recommendation systems.