Collaborative filtering algorithm using user background information
结合用户背景信息的协同过滤推荐算法

Aiming at the difficulty of data sparsity in personalized recommendation systems, a new collaborative filtering algorithm using user background information was presented. The algorithm took full advantage of user data and domain knowledge in hand, modeled user similarity based on user background information and filled in the user-item rating matrix in advance before the traditional collaborative filtering. The experimental results show that the new algorithm can improve the recommendation accuracy efficiently and will not cause bottleneck on performance.