Workbook
Recommendation Systems
A deep dive into the theory, algorithms, and engineering of modern recommendation systems.
Chapters
Introduction to Recommenders
We encounter recommendation systems every day: the next video that autoplay, the products suggested at checkout, the songs that fill your Discover Weekly. In this chapter we explore why recommendation systems exist, what data they rely on, and the classic algorithmic families that defined the field before deep learning.
Neural Network Based Recommenders
Deep learning transformed recommendation systems by enabling automatic feature discovery, non-linear interaction modeling, and scaling to billions of users and items. This chapter walks through the major neural architectures including matrix factorization and NCF to autoencoders, graph neural networks, and transformer-based sequential models.
Recommenders in Practice
Building a recommender that works in a research notebook is very different from one that runs reliably in production. This chapter covers the practical challenges of data preparation, offline and online evaluation metrics, and the unique deployment considerations that recommenders face: real-time serving, model freshness, and continuous learning.