Supervised Collaborative Filtering Based on Ridge Alternating Least Squares and Iterative Projection Pursuit

doi:10.1109/ACCESS.2017.2688449

	Supervised Collaborative Filtering Based on Ridge Alternating Least Squares and Iterative Projection Pursuit
	Chen, Bo-Wei 1; Ji, Wen 2; Rho, Seungmin 3; Gu, Yu 4
	2017
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	5 页码:6600-6607
摘要	This paper presents a supervised data imputation based on the class-dependent matrix factors, which are generated during matrix factorization. The proposed ridge alternating least squares imputation uses class information to create substituted values, which approximate the characteristics of their corresponding classes, for missing entries. In the training phase, the incomplete data with label information are divided into different classes based on their labels, such that basis matrices become class-dependent. Subsequently, iterative projection pursuit is proposed to perform imputation for testing data by computing the linear combination of these class-dependent basis matrices and their corresponding reconstruction weights. The class-dependent basis matrix with the minimum loss during reconstruction is regarded as the correct imputation for a testing sample, of which the substituted values are derived from the matrix factors of its class. Experiments on open data sets showed that the proposed method successfully decreased the imputation error by 40.52% on average, better than typical unsupervised collaborative filtering, while maintaining classification accuracy.
关键词	Incomplete data analysis privacy preservation supervised collaborative filtering collaborative filtering (CF) alternating least squares (ALS) supervised data imputation data imputation singular value decomposition (SVD) supervised nonnegative matrix factorization (NMF) recommendation system low-rank matrix approximation matrix completion matrix factorization iterative projection pursuit
DOI	10.1109/ACCESS.2017.2688449
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61572466] ; Beijing Natural Science Foundation[4162059]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000401431300131
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7186
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ji, Wen; Gu, Yu
作者单位	1.Monash Univ, Sch Informat Technol, Melbourne, Vic 3800, Australia 2.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China 3.Sungkyul Univ, Dept Media Software, Anyang 430742, South Korea 4.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Chen, Bo-Wei,Ji, Wen,Rho, Seungmin,et al. Supervised Collaborative Filtering Based on Ridge Alternating Least Squares and Iterative Projection Pursuit[J]. IEEE ACCESS,2017,5:6600-6607.
APA	Chen, Bo-Wei,Ji, Wen,Rho, Seungmin,&Gu, Yu.(2017).Supervised Collaborative Filtering Based on Ridge Alternating Least Squares and Iterative Projection Pursuit.IEEE ACCESS,5,6600-6607.
MLA	Chen, Bo-Wei,et al."Supervised Collaborative Filtering Based on Ridge Alternating Least Squares and Iterative Projection Pursuit".IEEE ACCESS 5(2017):6600-6607.