Effects of preprocessing and training biases in latent factor models for recommender systems

doi:10.1016/j.neucom.2017.10.040

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 大数据挖掘及应用中心

	Effects of preprocessing and training biases in latent factor models for recommender systems
	Yuan, Ye; Luo, Xin; Shang, Ming-Sheng
刊名	NEUROCOMPUTING
	2018
卷号	275 页码:2019-2030
ISSN号	0925-2312
DOI	10.1016/j.neucom.2017.10.040
通讯作者	Shang, MS (reprint author), Chinese Acad Sci, Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China. ; Luo, X (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China.
英文摘要	Latent factor (LF)-based models are highly efficient in addressing high-dimensional and sparse (HiDS) matrices raised in big-data-related applications like recommender systems. While linear biases have proven to be effective in improving the prediction accuracy and computational efficiency of LF models, their individual and combinational effects in such performance gain remain unclear. To address this issue, this work aims at studying the effects of linear biases in LF models for recommender systems. Based on careful investigations into existing methods, we categorize frequently adopted biases into two classes, i.e., preprocessing bias (PB) and training bias (TB). Subsequently, we deduce the training objectives and parameter updating rules of LF models with different PB and TB combinations. Experimental results on three HiDS matrices generated by real recommender systems show that (a) each PB/TB does have positive/negative effects in the performance of an LF model; (b) Such effects are partially data dependent, however, some specific PB/TB can bring stable performance gain to an LF model; and (c) several PB and TB combinations appear significantly more effective in improving an LF model's performance when compared to their peers. (C) 2017 Elsevier B.V. All rights reserved.
语种	英语
WOS记录号	WOS:000418370200189
内容类型	期刊论文
源URL	[http://172.16.51.4:88/handle/2HOD01W0/83]
专题	大数据挖掘及应用中心
作者单位	(1) Chinese Acad Sci, Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Ye,Luo, Xin,Shang, Ming-Sheng. Effects of preprocessing and training biases in latent factor models for recommender systems[J]. NEUROCOMPUTING,2018,275:2019-2030.
APA	Yuan, Ye,Luo, Xin,&Shang, Ming-Sheng.(2018).Effects of preprocessing and training biases in latent factor models for recommender systems.NEUROCOMPUTING,275,2019-2030.
MLA	Yuan, Ye,et al."Effects of preprocessing and training biases in latent factor models for recommender systems".NEUROCOMPUTING 275(2018):2019-2030.