Collaborative filtering recommendation based on user clustering in personalization service
个性化服务中基于用户聚类的协同过滤推荐

Collaborative filtering is the most successful technology for building recommendation systems.Unfortunately,the efficiency of this method declines linearly with the number of users and items.A collaborative filtering recommendation algorithm based on user clustering was employed to solve this problem.Users were clustered based on users' ratings on items,then the nearest neighbors of target user can be found in the user clusters most similar to the target user.Based on the algorithm,this paper proposed that the collaborative filtering algorithm should be divided into two stages: to compute the similar coefficient and to produce recommendation.The first stage was done in the off-line phase and thus the computation in the on-line recommendation phase was reduced and the speed of on-line recommendation system was increased.And this paper also improved the initial center point's selection of K-Means clustering algorithm.