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	An Instance-Frequency-Weighted Regularization Scheme for Non-Negative Latent Factor Analysis on High-Dimensional and Sparse Data
	Luo, Xin1; Wang, Zidong2; Shang, Mingsheng3,4
	2021-06-01
摘要	High-dimensional and sparse (HiDS) data with non-negativity constraints are commonly seen in industrial applications, such as recommender systems. They can be modeled into an HiDS matrix, from which non-negative latent factor analysis (NLFA) is highly effective in extracting useful features. Preforming NLFA on an HiDS matrix is ill-posed, desiring an effective regularization scheme for avoiding overfitting. Current models mostly adopt a standard L-2 scheme, which does not consider the imbalanced distribution of known data in an HiDS matrix. From this point of view, this paper proposes an instancefrequency-weighted regularization (IR) scheme for NLFA on HiDS data. It specifies the regularization effects on each latent factors with its relevant instance count, i.e., instance-frequency, which clearly describes the known data distribution of an HiDS matrix. By doing so, it achieves finely grained modeling of regularization effects. The experimental results on HiDS matrices from industrial applications demonstrate that compared with an L-2 scheme, an IR scheme enables a resultant model to achieve higher accuracy in missing data estimation of an HiDS matrix.
关键词	High-dimensional and sparse (HiDS) data industrial application instance-frequency non-negative latent factor analysis (NLFA) recommender system regularization
DOI	10.1109/TSMC.2019.2930525
发表期刊	IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN	2168-2216
卷号	51期号:6页码:3522-3532
通讯作者	Shang, Mingsheng(msshang@cigit.ac.cn)
收录类别	SCI
WOS记录号	WOS:000652103000018
语种	英语