An investigation of the application of graph neural networks in recommendation systems

• Main Author: Koh, Jaylene Jia Ying
• Other Authors: Luu Anh Tuan
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
• Online Access: https://hdl.handle.net/10356/171973
• Institution: Nanyang Technological University

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 Neural Networks extend this concept by considering more intricate relations within graphs. Hence, in this paper, we would explore the use of three types of state of the arts models: Graph Convolution Network, Graph Attention Network and GraphSAGE, to enhance a latent factor model by the graph structure and interactions.