I really like the depth Eugene Yan provides in this overview. Back up a sec.:
“Personalization is the process of customizing each individual’s experience. It’s how an electronics geek gets different recommendations from a cooking hobbyist, and how they might get different results from the same search query (e.g., “Apple”)”
This is the problem, and Eugene provides a nice little summary at the end which I have to share with you:
When to use which? Here’s a rough heuristic:
- Want to continuously explore while minimizing regret? Bandits
- Starting with neural recsys and want something simple? Embeddings+MLP
- Have long-term user histories and sequences? Sequential
- Have sparse behavior data but lots of item/user metadata? Graphs
- Want generic embeddings for multiple problems? User models
Now if you want to know some of the details, just dive right into the article which is written really well!
Btw. I did also enjoy Eugene’s welcome series to his newsletter “How to be an effective data scientist”.