An investigation of the application of graph neural networks in recommendation systems
Matrix Factorization, popularized by the Netflix Prize, has established itself as the prevailing method for recommendation systems based on latent factor models. While traditional latent factor models like matrix factorization focus on capturing latent factors using linear algebra techniques, Graph...
Saved in:
Main Author: | Koh, Jaylene Jia Ying |
---|---|
Other Authors: | Luu Anh Tuan |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/171973 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Federated graph neural network
by: Koh, Tat You @ Arthur
Published: (2021) -
Federated learning for graph neural networks
by: Yan, Yige
Published: (2023) -
Benchmarking novel graph neural networks
by: Bhagwat, Abhishek
Published: (2021) -
The use of knowledge graph for recommendation explanation
by: Yap, Desmond Qing Yang
Published: (2023) -
Interpretable fuzzy-embedded deep neural network with its application in stock trading
by: Koh, Amadeus Ying Jie
Published: (2023)