Generalized Nesterov's Acceleration-Incorporated, Non-Negative and Adaptive Latent Factor Analysis

Xin Luo, Yue Zhou, Zhigang Liu, Lun Hu, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2809-2823
Number of pages15
JournalIEEE Transactions on Services Computing
Volume15
Issue number5
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • Services computing
  • big data
  • high-dimensional and sparse matrix
  • latent factor analysis
  • missing data
  • non-negative latent factor model
  • recommender system
  • service application

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