By John D. Sutter, CNN

(CNN) — Netflix recently lifted the hood on its recommendation algorithm, which helps people who use that video-streaming service to find movies and shows that they may not know about but that they would like.

The most interesting stat the company listed on its corporate blog: 75% of the videos people watch are found via some kind of recommendation that the company employs on its apps and website. That means most pieces of content people end up watching on Netflix are found with the help of a computer equation that thinks it know what you want to watch – or via Facebook, for people outside the United States (Netflix doesn’t connect with Facebook in the U.S. because some say the Video Privacy Protection Act prohibits video rental records from being shared. There is some confusion over when and how that law applies.)

In any event, here’s what Netflix has to say about the popularity of its digital recommendations:

We have adapted our personalization algorithms to this new scenario in such a way that now 75% of what people watch is from some sort of recommendation. We reached this point by continuously optimizing the member experience and have measured significant gains in member satisfaction whenever we improved the personalization for our members.

That’s pretty remarkable. But the company admits it still has some kinks to work out.

One of them: Tailoring recommendations to individual Netflix users instead of to the entire household. Often, a family will sign up for a single Netflix account, so recommendations sometimes get muddled between what mom, dad and the kids want to watch. Netflix says in its Friday blog post that it addresses this issue by showing a “diversity” of content that would be appropriate for any member of the family.

It is important to keep in mind that Netflix’ personalization is intended to handle a household that is likely to have different people with different tastes. That is why when you see your Top 10, you are likely to discover items for dad, mom, the kids, or the whole family. Even for a single person household we want to appeal to your range of interests and moods. To achieve this, in many parts of our system we are not only optimizing for accuracy, but also for diversity.

Do you use Netflix’s recommendation engine – or others like it on sites like Amazon? If so, what do you think? Where do these equations fall short and when have they gotten it right?