A Regularization-Adaptive Non-negative Latent Factor Analysis-based Model for Recommender Systems
Document Type
Conference Proceeding
Publication Date
9-1-2020
Abstract
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.
Identifier
85093982328 (Scopus)
ISBN
[9781728158716]
Publication Title
Proceedings of the 2020 IEEE International Conference on Human Machine Systems Ichms 2020
External Full Text Location
https://doi.org/10.1109/ICHMS49158.2020.9209550
Grant
61602352
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Chen, Jiufang; Luo, Xin; and Zhou, Meng Chu, "A Regularization-Adaptive Non-negative Latent Factor Analysis-based Model for Recommender Systems" (2020). Faculty Publications. 5053.
https://digitalcommons.njit.edu/fac_pubs/5053
