RL is a good theoretical solution for personalization: given a user state, select an action that maximizes a long term reward (eg. revenue/engagement.) It’s tricky building the implementations because unlike Go/Chess/Atari it’s hard to simulate humans. So you have to train the agents with batches of data offline (ie. using historic data from the agent’s past actions.) This is challenging because you don’t get as many chances to try different hyper parameters. It’s starting to be used more in industry though.