I think the fact that Netflix saw a 200% increase in "thumbs" ratings vs "stars" in the A/B test makes it much better from a machine learning perspective, even

I think the fact that Netflix saw a 200% increase in "thumbs" ratings vs "stars" in the A/B test makes it much better from a machine learning perspective, even if it might at first seem like the data is of "lower quality". One of the biggest problems for recommender systems is that the data is extremely sparse - most users will have only rated a tiny proportion of films, and most films will only have been rated by a tiny proportion of users. This is just my opinion now, but having studied recommender systems in a decent amount of depth, I don't think the design of the algorithm will need to change. The current techniques that provide the best results use matrix factorisation to simultaneously learn characteristics of films and how much each user likes each characteristic. My intuition would be that the algorithm can learn a lot more about a user from 3 up/down-ratings than from 1 star-rating, and the only reason Netflix are doing this is so that they can provide better recommendations so it's almost certainly the case. TL;DR: All else being equal, 5-star ratings carry more signal for any machine learning algorithm, but the fact that thumbs up/down will result in 200% more data is a lot more significant than the delta in the signal.