Latent Factor Analysis for High-dimensional and Sparse Matrices: A particle swarm optimization-based approach

Preface
Contents
Chapter 1: Introduction
	1.1 Background
	1.2 Preliminaries
		1.2.1 Symbol and Abbreviation Appointment
		1.2.2 An LFA Model
		1.2.3 Particle Swarm Optimization
	1.3 Book Organization
	References
Chapter 2: Learning Rate-Free Latent Factor Analysis via PSO
	2.1 Overview
	2.2 An LFA Model with SGD Algorithm
	2.3 The Proposed L2FA Model
		2.3.1 Learning Rate Adaptation via PSO
		2.3.2 Algorithm Design and Analysis
	2.4 Experimental Results and Analysis
		2.4.1 General Settings
		2.4.2 Performance Comparison
		2.4.3 Effect of Swarm Size
		2.4.4 Summary
	2.5 Conclusions
	References
Chapter 3: Learning Rate and Regularization Coefficient-Free Latent Factor Analysis via PSO
	3.1 Overview
	3.2 An SGD-Based LFA Model
	3.3 The Proposed LRLFA Model
		3.3.1 Learning Rate and Regularization Coefficient Adaptation via PSO
		3.3.2 Linearly Decreasing Inertia Weight Incorporation
		3.3.3 Algorithm Design and Analysis
	3.4 Experimental Results and Analysis
		3.4.1 General Settings
		3.4.2 Effect of Swarm Size
		3.4.3 Performance Comparison
		3.4.4 Summary
	3.5 Conclusions
	References
Chapter 4: Regularization and Momentum Coefficient-Free Non-negative Latent Factor Analysis via PSO
	4.1 Overview
	4.2 An SLF-NMU-Based NLFA Model
	4.3 The Proposed GALFA Model
		4.3.1 A Generalized Learning Objective
		4.3.2 A Generalized SLF-NMU-Based Learning Rule
		4.3.3 Generalized-Momentum Incorporation
		4.3.4 Regularization and Momentum Coefficient Adaptation via PSO
		4.3.5 Algorithm Design and Analysis
	4.4 Experimental Results and Analysis
		4.4.1 General Settings
		4.4.2 Parameter Sensitivity
		4.4.3 Comparison with State-of-the-Art Models
		4.4.4 Summary
	4.5 Conclusions
	References
Chapter 5: Advanced Learning Rate-Free Latent Factor Analysis via P2SO
	5.1 Overview
	5.2 An SGD-Based LFA Model
	5.3 The Proposed AL2FA Model
		5.3.1 A P2SO Algorithm
		5.3.2 Learning Rate Adaptation via P2SO
		5.3.3 Algorithm Design and Analysis
	5.4 Experimental Results and Analysis
		5.4.1 General Settings
		5.4.2 Effect of ρ
		5.4.3 Comparison Results
		5.4.4 Summary
	5.5 Conclusions
	References
Chapter 6: Conclusion and Future Directions
	6.1 Conclusion
	6.2 Discussion
	References