计算机应用与软件2017，Vol.34Issue(7)：257-261,5.DOI:10.3969/j.issn.1000-386x.2017.07.047
基于联合聚类和C-RA组合相似度的协同过滤算法
COLLABORATIVE FILTERING ALGORITHM BASED ON CO-CLUSTERING AND C-RA COMBINED SIMILARITY
摘要
Abstract
In order to overcome the sparse data and cold start of traditional collaborative filtering recommendation algorithm, a collaborative filtering algorithm based on co-clustering and C-RA combined similarity is proposed.First, co-clustering algorithm is used to simultaneously obtain user and item neighborhoods.Secondly, the result of co-clustering is used on rating matrix.Finally, C-RA combined similarity is used to calculate the similarity of users and recommend.Experimental results show that the proposed method not only effectively improves the accuracy of the recommended results, but also solves problems of user cold start and data sparsity.关键词
协同过滤/冷启动/数据稀疏性/联合聚类/C-RAKey words
Collaborative filtering/ Cold start/ Data sparsity/ Co-clustering/ C-RA分类
信息技术与安全科学引用本文复制引用
赵文涛,王春春,成亚飞..基于联合聚类和C-RA组合相似度的协同过滤算法[J].计算机应用与软件,2017,34(7):257-261,5.基金项目
河南省科技攻关项目(142402210435) （142402210435）
河南省高等学校矿山信息化重点学科开放基金项目(ky2012-02). （ky2012-02）
