TY - GEN
T1 - Accurate Latent Factor Analysis via Dynamic-Neighbor-cooperated Hierarchical Particle Swarm Optimizers
AU - Chen, Jia
AU - Yi, Xianchun
AU - Hu, Yang
AU - Liu, Yuanyi
AU - Zhang, Renyu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - High-Dimensional and Incomplete (HDI) matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis (LFA) model. The performance of an LFA model heavily rely on its optimization process. Thereby, some prior studies employ the Particle Swarm Optimization (PSO) to enhance an LFA model's optimization process. However, the particles within the swarm follow the static evolution paths and only share the global best information, which limits the particles' searching area to cause sub-optimum issue. To address this issue, this paper proposes a Dynamic-neighbor-cooperated Hierarchical PSO-enhanced LFA (DHPL) model with two-fold main ideas. First is the neighbor-cooperated strategy, which enhances the randomly chosen neighbor's velocity for particles' evolution. Second is the dynamic hyper-parameter tunning. Extensive experiments on two benchmark datasets are conducted to evaluate the proposed DHPL model. The results substantiate that DHPL achieves a higher accuracy without hyper-parameters tunning than the existing PSO-incorporated LFA models in representing an HDI matrix.
AB - High-Dimensional and Incomplete (HDI) matrices, which usually contain a large amount of valuable latent information, can be well represented by a Latent Factor Analysis (LFA) model. The performance of an LFA model heavily rely on its optimization process. Thereby, some prior studies employ the Particle Swarm Optimization (PSO) to enhance an LFA model's optimization process. However, the particles within the swarm follow the static evolution paths and only share the global best information, which limits the particles' searching area to cause sub-optimum issue. To address this issue, this paper proposes a Dynamic-neighbor-cooperated Hierarchical PSO-enhanced LFA (DHPL) model with two-fold main ideas. First is the neighbor-cooperated strategy, which enhances the randomly chosen neighbor's velocity for particles' evolution. Second is the dynamic hyper-parameter tunning. Extensive experiments on two benchmark datasets are conducted to evaluate the proposed DHPL model. The results substantiate that DHPL achieves a higher accuracy without hyper-parameters tunning than the existing PSO-incorporated LFA models in representing an HDI matrix.
KW - Dynamic Neighbor Cooperation
KW - High-dimensional and Incomplete Matrix
KW - Latent Factor Analysis
KW - Particle Swarm Optimization
UR - https://www.scopus.com/pages/publications/85146926578
U2 - 10.1109/ICNSC55942.2022.10004108
DO - 10.1109/ICNSC55942.2022.10004108
M3 - 会议稿件
AN - SCOPUS:85146926578
T3 - ICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control: Autonomous Intelligent Systems
BT - ICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022
Y2 - 15 December 2022 through 18 December 2022
ER -