Abstract
In this research, would bring a description and comparison of how the Deep learning-based Graph neural Network actually outperforms the other similar recommender system like collaborative filtering, content-based filtering, SVD, Matrix Factorization and few others. This is basically achieved by exposing the correct relation between the objects through a graph architecture and the dependency and inter correlation between them. Would also like to share an in-depth analysis and understanding of how the Graph architecture works and the underlying theories. This could be either a TensorFlow based architecture or a Pytorch based architecture but in this paper will mainly focus on the TensorFlow one for its flexibility and cloud friendly nature for adopting in any framework.
Keywords
- Recommendation System
- TensorFlow
- Graph Architecture
- Neural Network
References
- Graph neural networks: A review of methods and applications (arxiv.org)
- A Gentle Introduction to Graph Neural Networks (distill.pub)
- https://arxiv.org/pdf/1901.00596.pdf
- Graph Neural Network and Some of GNN Applications: Everything You Need to Know - neptune.ai
- Graph Neural Networks: A learning journey since 2008— Part 1 | by Stefano Bosisio | Towards Data Science