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浙江大学学报(工学版)
浙江大学学报(工学版)
通信工程、自动化技术     
基于结构投影非负矩阵分解的协同过滤算法
居斌1,2, 钱沄涛1, 叶敏超1
1.浙江大学 计算机学院,浙江 杭州 310027；2.浙江省卫生信息中心,浙江 杭州 310006
Collaborative filtering algorithm based on structured projective nonnegative matrix factorization
JU Bin1,2, QIAN Yun-tao1, YE Min-chao1
1.College of Computer Science, Zhejiang University, Hangzhou 310027, China; 2.Health Information Center of Zhejiang Province, Hangzhou 310006, China
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摘要：

针对在协同过滤算法中,传统矩阵分解技术在降维过程中会破坏数据相邻结构的问题,提出基于结构投影非负矩阵分解的协同过滤算法(CF-SPNMF).该算法包含离线学习和在线搜索2个阶段.在离线学习阶段,通过对用户评分矩阵的投影非负矩阵分解,同时保留用户特征的聚类结构,得到低维的用户潜在兴趣因子.在线搜索阶段,将用户潜在兴趣因子进行余弦相似性匹配,发现目标用户与训练样本用户之间兴趣最相似的邻域集合.在实际数据集上的实验结果表明,提出的CF-SPNMF算法与单纯使用矩阵分解和单纯在原评分矩阵上进行用户聚类的推荐算法相比,能够更有效地预测用户实际评分.
Abstract:

In collaborative filtering algorithm, the classical matrix factorization may destroy the adjacent structures among data points from high dimension to low dimension. A  novel collaborative filtering algorithm  based on structured projective nonnegative matrix factorization (CF-SPNMF)  was proposed in order to overcome the problem. The algorithm contains both offline learning and online searching. In the offline learning stage, projective nonnegative matrix factorization was applied to obtain the low dimensional latent factors of user preference without changing the intrinsic structure of users cluster. In the online searching stage, cosine similarity was used to measure the similarity between the target user and training users based on the latent factors inferred in the offline stage. Then the most similar neighbor set was further found. The extensive experiments on real-world data set demonstrate that the proposed CF-SPNMF achieves better rating prediction performance than traditional methods using either matrix factorization or users clustering in original rating matrix．
出版日期: 2015-09-10
:  TP 181  
基金资助:

浙江省自然科学基金资助项目（Y1101359);国家科技支撑计划资助项目（2011BAD24B03)
通讯作者: 钱沄涛,男,教授,博导     E-mail: ytqian@zju.edu.cn
作者简介: 居斌（1975－),男,博士生,从事机器学习、数据挖掘的研究.E-mail:jubin_hz@163.com
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引用本文:

居斌, 钱沄涛, 叶敏超. 基于结构投影非负矩阵分解的协同过滤算法[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2015.07.017.

JU Bin, QIAN Yun-tao, YE Min-chao. Collaborative filtering algorithm based on structured projective nonnegative matrix factorization. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2015.07.017.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2015.07.017    或    http://www.zjujournals.com/eng/CN/Y2015/V49/I7/1319

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