An adaptive latent factor model via particle swarm optimization for high-dimensional and sparse matrices

doi:10.1109/SMC.2019.8914673

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

	An adaptive latent factor model via particle swarm optimization for high-dimensional and sparse matrices
	Chen, Sili 1,2; Yuan, Ye 1; Wang, Jin 2
	2019
会议日期	October 6, 2019 - October 9, 2019
会议地点	Bari, Italy
DOI	10.1109/SMC.2019.8914673
页码	1738-1743
英文摘要	Latent factor (LF) models are greatly efficient in extracting valuable knowledge from High-Dimensional and Sparse (HiDS) matrices which are commonly seen in many industrial applications. Stochastic gradient descent (SGD) is an efficient scheme to build an LF model, yet its convergence rate depends vastly on the learning rate which should be tuned with care. Therefore, automatic selection of an optimal learning rate for an SGD-based LF model is a significant issue. To address it, this study incorporates the principle of particle swarm optimization (PSO) into an SGD-based LF model for searching an optimal learning rate automatically. With it, we further propose an adaptive Latent Factor (ALF) model. Empirical studies on two HiDS matrices from industrial applications indicate that an ALF model obviously outperforms an LF model in terms of convergence rate, and maintains competitive prediction accuracy for missing data. © 2019 IEEE.
会议录	2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
语种	英语
ISSN号	1062922X
内容类型	会议论文
源URL	[http://119.78.100.138/handle/2HOD01W0/9801]
专题	大数据挖掘及应用中心
作者单位	1.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing; 400714, China 2.Computer School of China West Normal University, Nanchong, Sichuan; 637002, China;
推荐引用方式 GB/T 7714	Chen, Sili,Yuan, Ye,Wang, Jin. An adaptive latent factor model via particle swarm optimization for high-dimensional and sparse matrices[C]. 见:. Bari, Italy. October 6, 2019 - October 9, 2019.