一个解决协同过滤推荐系统相关问题的新算法  

New algorithm for collaborative filtering recommendation system-related problem solving

作　　者：陈琦[1] 吕杰[1] 张世超[1] 

机构地区：[1]天津大学电子信息工程学院,天津300072

出　　处：《电子测量技术》2016年第5期66-69,共4页Electronic Measurement Technology

摘　　要：针对大数据应用中用户协同过滤推荐系统存在的扩展性与稀疏性问题,提出融合奇异值分解与聚类的SBKCF算法。算法采用改进的皮尔逊相似度度量用户间的相似度,通过对降维后的用户进行聚类,并遍历用户的最临近簇生成推荐列表。实验结果表明,提出的算法能够有效完成个性化推荐,在一定程度上解决用户协同过滤推荐系统中存在的扩展性与稀疏性问题。In this paper,a fusion of singular value decomposition and clustering algorithm named SVD biKmeans collaborative filtering（SBK-CF）is proposed,in order to solve the scalability and sparsity problems in user based collaborative filtering system.The algorithm adopts improved Pearson similarity metric formula to measure similarity between users,and clustering those users which have been dimension reducted,then generates the recommendation list through the nearest neighbor cluster of users.Experimental results show that the proposed algorithm can effectively accomplish the mission of personalized recommendation and solve the scalability and sparsity problems in user-based collaborative filtering system.

关 键 词：协同过滤 扩展性 稀疏性 奇异值分解 聚类 

分 类 号：TN82[电子电信—信息与通信工程]