  Stochastic gradient descent (SGD) algorithm is an effective learning strategy
to build a latent factor analysis (LFA) model on a high-dimensional and
incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is
commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e,
learning rate and regularization coefficient, self-adaptation. However, a
standard PSO algorithm may suffer from accuracy loss caused by premature
convergence. To address this issue, this paper incorporates more historical
information into each particle's evolutionary process for avoiding premature
convergence following the principle of a generalized-momentum (GM) method,
thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a
GM-PSO-based LFA (GMPL) model is further achieved to implement efficient
self-adaptation of hyper-parameters. The experimental results on three HDI
matrices demonstrate that the GMPL model achieves a higher prediction accuracy
for missing data estimation in industrial applications.
