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Generalized Nesterov's Acceleration-Incorporated, Non-Negative and Adaptive Latent Factor Analysis
Luo, Xin1; Zhou, Yue1,2; Liu, Zhigang1,2; Hu, Lun3; Zhou, MengChu4,5,6
2022-09-01
摘要A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service-oriented applications. However, its convergence rate is slow. To address this issue, this study proposes a Generalized Nesterov's acceleration-incorporated, Non-negative and Adaptive Latent Factor (GNALF) model. It results from a) incorporating a generalized Nesterov's accelerated gradient (NAG) method into an SLF-NMU algorithm, thereby achieving an NAG-incorporated and element-oriented non-negative (NEN) algorithm to perform efficient parameter update; and b) making its regularization and acceleration parameters self-adaptive via incorporating the principle of a particle swarm optimization algorithm into the training process, thereby implementing a highly adaptive and practical model. Empirical studies on six large sparse matrices from different recommendation service applications show that a GNALF model achieves very high convergence rate without the need of hyper-parameter tuning, making its computational efficiency significantly higher than state-of-the-art models. Meanwhile, such efficiency gain does not result in accuracy loss, since its prediction accuracy is comparable with its peers. Hence, it can better serve practical service applications with real-time demands.
关键词Computational modeling Acceleration Sparse matrices Adaptation models Training Data models Convergence Services computing service application big data latent factor analysis non-negative latent factor model high-dimensional and sparse matrix recommender system missing data
DOI10.1109/TSC.2021.3069108
发表期刊IEEE TRANSACTIONS ON SERVICES COMPUTING
ISSN1939-1374
卷号15期号:5页码:2809-2823
通讯作者Hu, Lun(hulun@ms.xjb.ac.cn) ; Zhou, MengChu(zhou@njit.edu)
收录类别SCI
WOS记录号WOS:000865092800024
语种英语