Personalisation and recommendation are one of the most most effective applications of machine learning.

They allow businesses to tailor products and services to individuals based on a combination of user behaviours and item features. Eugene Yan has written a useful review of some of the main approaches. He splits them into 5 groups:

➡ Embeddings + MLP: A good simple starting point

➡ Bandits: A way to balance exploration and exploitation

➡ Sequential: If you've got long user histories

➡ Graphs: Not much behaviour data but lots of item/user metadata

➡ User models: Generic embedding for multiple problems

🛎️ Why this matters: There have been a lot of developments in this field: here is a really useful map.