A Regularization-adaptive Non-negative Latent Factor Analysis-based Model For Recommender Systems

Non-negative latent factor analysis (NLFA) can high-efficiently extract useful information from high dimensional and sparse (HiDS) matrices often encountered in recommender systems (RSs). However, an NLFA-based model requires careful tuning of regularization coefficients, which is highly expensive i...

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Bibliographic Details
Published in2020 IEEE International Conference on Human-Machine Systems (ICHMS) pp. 1 - 6
Main Authors Chen, Jiufang, Luo, Xin, Zhou, MengChu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2020
Subjects
Adaptation models
Analytical models
Big Data
Computational modeling
High-Dimensional and Sparse Matrix
Non-negative Latent Factor Analysis
Particle swarm optimization
Recommender System
Self-adaptive Model
Sparse matrices
Tuning
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Summary:Non-negative latent factor analysis (NLFA) can high-efficiently extract useful information from high dimensional and sparse (HiDS) matrices often encountered in recommender systems (RSs). However, an NLFA-based model requires careful tuning of regularization coefficients, which is highly expensive in both time and computation. To address this issue, this study proposes an adaptive NLFA-based model whose regularization coefficients become self-adaptive via particle swarm optimization. Experimental results on two HiDS matrices indicate that owing to such self-adaptation, it outperforms an NLFA model in terms of both convergence rate and prediction accuracy for missing data estimation.
DOI:10.1109/ICHMS49158.2020.9209550