@inproceedings{cc1c59e70d2e4eb8bc822fd14c0bbe84,
title = "A Regularization-Adaptive Non-negative Latent Factor Analysis-based Model for Recommender Systems",
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.",
keywords = "High-Dimensional and Sparse Matrix, Non-negative Latent Factor Analysis, Particle Swarm Optimization, Recommender System, Self-Adaptive Model",
author = "Jiufang Chen and Xin Luo and Zhou, {Meng Chu}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 1st IEEE International Conference on Human-Machine Systems, ICHMS 2020 ; Conference date: 07-09-2020 Through 09-09-2020",
year = "2020",
month = sep,
doi = "10.1109/ICHMS49158.2020.9209550",
language = "English (US)",
series = "Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Giancarlo Fortino and Fei-Yue Wang and Andreas Nurnberger and David Kaber and Rino Falcone and David Mendonca and Zhiwen Yu and Antonio Guerrieri",
booktitle = "Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020",
address = "United States",
}