中国科学院重庆绿色智能技术研究院机构知识库

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	Effects of preprocessing and training biases in latent factor models for recommender systems
	Yuan, Ye; Luo, Xin; Shang, Ming-Sheng
	2018
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.
DOI	10.1016/j.neucom.2017.10.040
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	275页码:2019-2030
收录类别	SCI
WOS记录号	WOS:000418370200189
语种	英语