An investigation of the application of graph neural networks in recommendation systems - Koh, Jaylene Jia Ying (Nanyang Technological University, 2023)
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.