Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge
Overfitting in a deep neural network leads to low recommendation precision and high loss. To mitigate these issues in a deep neural network-based recommendation algorithm, we propose a recommendation algorithm, LG-DropEdge, joint light graph convolutional network, and the DropEdge. First, to reduce...
Saved in:
Main Authors: | Haicheng Qu, Jiangtao Guo, Yanji Jiang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2022/3843021 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-channel based edge-learning graph convolutional network
by: Shuai YANG, et al.
Published: (2022-09-01) -
Recommendation model combining review’s feature and rating graph convolutional representation
by: Hailin FENG, et al.
Published: (2022-03-01) -
Accurate multi-behavior sequence-aware recommendation via graph convolution networks.
by: Doyeon Kim, et al.
Published: (2025-01-01) -
Accurate multi-behavior sequence-aware recommendation via graph convolution networks
by: Doyeon Kim, et al.
Published: (2025-01-01) -
Spectral convolutional neural network chip for in-sensor edge computing of incoherent natural light
by: Kaiyu Cui, et al.
Published: (2025-01-01)