一种基于改进的层次聚类的协同过滤用户推荐算法研究

为了降低组用户推荐的计算时间,提出了一种改进的层次聚类协同过滤用户推荐算法。由于数据的稀疏性,传统的聚类方法在尝试划分用户群时效果不理想。考虑到传统聚类算法的聚类中心不变组内用户间相关度不高等问题,将用户进行聚类,然后按照分类计算出每个用户的推荐结果,在进行聚类的同时充分利用用户间的信息传递来增强组内用户的信息共享,最后将组内所有的用户的推荐结果进行聚合。最后仿真实验表明,本方法能够有效地提高推荐的准确度,比传统的协同过滤算法具有更高的执行效率。

Collaborative Filtering Recommendation Algorithm Based on Improved User Clustering

In order to reduce the computation time of group user recommendation,this paper proposed an improved k-means clustering collaborative filtering recommendation algorithm.Because of the sparsity of data,the effect of the traditional clustering methods is not ideal when trying to divide user group.This paper took into account that invariant group correlation between users in the clustering center of the traditional K-means algorithm is not high,made the user clustering,then according to the classification calculated recommended results of each user in the cluster,made full use of user information transmission between users to enhance information sharing within the group,and polymerized all user recommendation result of the group.Finally,simulation results show that the method proposed in this paper can effectively improve the accuracy of the recommendation,and it is more effective than traditional collaborative filtering algorithm.