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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)
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
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