一种基于用户聚类的协同过滤推荐算法

为解决传统协同过滤算法在生成推荐时的速度瓶颈问题,提出了一种基于用户聚类的协同过滤推荐算法。该算法将推荐过程分成了离线和在线两个部分。离线时,算法对基本用户数据进行预处理,并对基本用户聚类;在线时,算法利用已有的用户聚类寻找目标用户最近邻居,并产生推荐。实验表明,基于用户聚类的协同过滤推荐算法不仅加快了推荐生成速度,而且提高了推荐质量。

Collaborative filtering recommendation algorithm based on clustering basal users

To overcome the difficulty of the speed bottleneck of collaborative filtering(CF) algorithm used for generating recommendation,a CF algorithm based on clustering basal users is presented.The algorithm separates the procedure of recommendation into offline and online phases.In the offline phase,the data of basal users are preprocessed,and the basal users are clustered;while in the online phase,the nearest neighbors of an active user are found according to the basal user clusters,and the recommendation to the active user is produced.The experimental results show that the presented algorithm can improve the performance of CF systems in both the recommendation quality and efficiency.