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.
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Keywords
- Recommendation engine
- Deep Learning
- Graph Neural Network
- node2Vec
- Artificial Intelligence
- Tensorflow
- Pytorch
- word2vec
- skipgram
- CBOW
- Random walk
- Pointwise mutual info
- Tensorboard
- Multi layer perceptron
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 2008ā Part 1 | by Stefano Bosisio | Towards Data Science