摘要
为解决传统协同过滤算法在产生推荐时实时性较差性问题,提出了一种基于蚁群模糊聚类的协同过滤推荐算法.该算法将分两个步骤产生推荐.离线时,应用蚁群模糊聚类技术,对基本用户进行聚类;在线时,利用已有的用户蚁群聚类寻找目标用户的最近邻居,并产生推荐.实验表明,基于蚁群模糊聚类的协同过滤推荐算法能提高推荐产生的速度,即实时性得到了一定的提高.
To overcome the difficulty of timely of collaborative filtering algorithm used for generating recommendation,a collaborative filtering recommendation algorithm based on Ant Colony Vague clustering is presented.The algorithm separates the procedure of recommendation into offline and online phases.In the offline phase,the basal users are clustered by Ant Colony Vague clustering technology;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 sysytems in both the recommendation quality and efficiency.
出处
《湖南工程学院学报（自然科学版）》
2011年第4期55-58,共4页
Journal of Hunan Institute of Engineering(Natural Science Edition)
关键词
推荐算法
协同过滤
蚁群模糊聚类
MAE
recommendation algorithm
collaborative filtering
Ant Colony Vague clustering
MAE
