Summary

In this chapter, we introduced graph deep learning as an advanced approach to recommendation systems. You learned about the fundamental concepts of recommendation systems, including the different types and evaluation metrics.

Then, we delved into graph structures for representing user-item interactions, incorporating side information, and capturing temporal dynamics. Various graph-based recommendation models were explored, from MF with graph regularization to advanced GNN models. You also became familiar with a variety of training techniques, scalability challenges, and advanced topics such as explainability and the cold start problem in graph-based recommendation systems.

In the next chapter, we’re going to investigate the applications of graph learning in computer vision.