Advantages of Binary Rating System for Collaborative Filtering Recommender Systems
This article from Business Insider answers your question from the business perspective. 

They mention that the original problem is that the way Netflix used the 5-star rating was not standard within the industry:


  Netflix’s Cameron Johnson, who oversaw the shift, told Business
  Insider that it all came from the realization that Netflix had always
  used star ratings differently than the rest of the internet, but that
  this distinction wasn't clear to users.


And the way ratings were presented to the user discouraged them to contribute. Their solution was the thumbs up/thumbs down approach because it was more clear to the user that they were training an algorithm:


  So when looking for a replacement, Netflix wanted to make sure that
  was clear. That’s why Netflix settled on “thumbs up/down," which is
  widely understood to imply that you are training an algorithm to know
  what you like, Johnson said.


I suspect there is more than that thought, and maybe binary data is simply "good enough".

This chapter from Mining of Massive Datasets has some interesting considerations on ratings versus boolean utility matrices. 

From page 339:


  If the utility matrix is not Boolean, e.g., ratings 1–5, then we can
  weight the vectors representing the profiles of items by the utility
  value. It makes sense to normalize the utilities by subtracting the
  average value for a user.


Specially when treating rating matrices one need to be aware that different users may have different understanding of ratings, hence you will most likely normalise the matrix before calculating the recommendations, while for binary data this is not required.