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Table 3 Summary of the compared models

From: An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis

Parameters

Models

Descriptions

fixed η and

λ (λP = λP) model

M1

SGD. A fundamental SGD-based LFA model that converges very fast with the resilience for the HDI data analysis

M2

Moment SGD. For a better update of the current one in the process of the SGD-based LFA model, it incorporates the previous updates into the current one in the way of momentum

M3

Adam. This adaptive moment estimation SGD method further considers exponentially decaying of square average and past stochastic gradients average

η-λ-(λP = λP)

adaptive

model

M4

ALF. An adaptive SGD-based LFA model that adopts standard PSO to adjust the learning rate in the model

M5

DELF. Differential evolution-based LF analysis model employs original differential evolution to conduct an SGD-based LFA model adaptively

η-λ-(λP ≠ λP) adaptive

model

M6

The proposed ADMA. Compared with DELF, the proposed model ADMA improves the original DE algorithm for a more precise prediction in an SGD-based LFA model with limited time