南京邮电大学学报（自然科学版）2017，Vol.37Issue(3)：93-99,7.DOI:10.14132/j.cnki.1673-5439.2017.03.013
基于用户限制聚类的协同过滤推荐算法
Collaborative filtering recommendation algorithm based on user restriction clustering
摘要
Abstract
Collaborative filtering recommendation algorithm is the key technology of personalized recommender systems,but there exist some problems such as ‘ data sparseness’ and ‘ cold start’ in the calculation process.A collaborative filtering recommendation algorithm based on user restriction clustering is proposed by using the clustering technique in data mining.Firstly,the similar users are clustered together by the restriction clustering technique,and the neighbor users are found in the clustering.Then,the improved collaborative filtering algorithm is used to make recommendations.Experimental results show that the algorithm improves the situation of ‘ data sparseness’ and ‘ cold start’,and has higher recommendation quality than the traditional collaborative filtering algorithm and the K-means user clustering collaborative filtering algorithm.关键词
数据挖掘/聚类算法/协同过滤/推荐算法Key words
data mining/clustering algorithm/collaborative filtering/recommendation algorithm分类
信息技术与安全科学引用本文复制引用
张松,张琳,王汝传..基于用户限制聚类的协同过滤推荐算法[J].南京邮电大学学报（自然科学版）,2017,37(3):93-99,7.基金项目
国家自然科学基金(61402241,61572260,61373017,61572261,61472192)、江苏省科技支撑计划(BE2015702)、江苏省普通高校研究生科研创新计划(CXLX12_0482)和南京邮电大学校级科研基金(NY217050)资助项目 （61402241,61572260,61373017,61572261,61472192）
