Accurate Latent Factor Analysis via Particle Swarm Optimizers

Document Type

Conference Proceeding

Publication Date

1-1-2021

Abstract

A stochastic-gradient-descent-based Latent Factor Analysis (LFA) model is highly efficient in representative learning of a High-Dimensional and Sparse (HiDS) matrix. Its learning rate adaptation is vital in ensuring its efficiency. Such adaptation can be realized with an evolutionary computing algorithm. However, a resultant model tends to suffer from two issues: a) the pre-mature convergence of the swarm of learning rates as caused by an adopted evolution algorithm, and b) the pre-mature convergence of the LFA model as caused jointly by evolution-based learning rate adaptation and an optimization algorithm. This paper focuses on the methods to address such issues. A Hierarchical Particle-swarm-optimization-incorporated Latent factor analysis (HPL) model with a two-layered structure is proposed, where the first layer pre-trains desired latent factors with a position-transitional particle-swarm-optimization-based LFA model, and the second layer performs latent factor refining with a newly-proposed mini-batch particle swarm optimizer. With such design, an HPL model can well handle the pre-mature convergence, which is supported by the positive experimental results achieved on HiDS matrices from industrial applications.

Identifier

85124266741 (Scopus)

ISBN

[9781665442077]

Publication Title

Conference Proceedings IEEE International Conference on Systems Man and Cybernetics

External Full Text Location

https://doi.org/10.1109/SMC52423.2021.9659218

ISSN

1062922X

First Page

2930

Last Page

2935

Grant

cstc2018jszx-cyzdX0041

Fund Ref

National Natural Science Foundation of China

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