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

doi:10.1109/ACCESS.2017.2688449

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Supervised Collaborative Filtering Based on Ridge Alternating Least Squares and Iterative Projection Pursuit
	Chen, Bo-Wei 2; Ji, Wen 3; Rho, Seungmin 1; Gu, Yu 4
刊名	IEEE ACCESS
	2017
卷号	5 页码:6600-6607
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
ISSN号	2169-3536
DOI	10.1109/ACCESS.2017.2688449
英文摘要	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.
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
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000401431300131
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/7185]
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.Sungkyul Univ, Dept Media Software, Anyang 430742, South Korea 2.Monash Univ, Sch Informat Technol, Melbourne, Vic 3800, Australia 3.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China 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.