From a post that was recently posted here[0]:

>While we're here, I just want to rant about Netflix, which is an odd case of starting off with a really good recommendation algorithm and then making it worse on purpose. Once upon a time, there was the Netflix prize, which granted $1 million to the best team that could predict people's movie ratings, based on their past ratings, with better accuracy than Netflix could themselves. (This not-so-shockingly resulted in a privacy fiasco when it turned out you could de-anonymize the data set that they publicly released, oops. Well, that's what you get when you long-term store people's personal information in a database.) Netflix believed their business depended on a good recommendation algorithm. It was already pretty good: I remember using Netflix around 10 years ago and getting several recommendations for things I would never have discovered, but which I turned out to like. That hasn't happened to me on Netflix in a long, long time. As the story goes, once upon a time Netflix was a DVD-by-mail service. DVD-by-mail is really slow, so it was absolutely essential that at least one of this week's DVDs was good enough to entertain you for your Friday night movie. Too many Fridays with only bad movies, and you'd surely unsubscribe. A good recommendation system was key. (I guess there was also some interesting math around trying to make sure to rent out as much of the inventory as possible each week, since having a zillion copies of the most recent blockbuster, which would be popular this month and then die out next month, was not really viable.) Eventually though, Netflix moved online, and the cost of a bad recommendation was much less: just stop watching and switch to a new movie. Moreover, it was perfectly fine if everyone watched the same blockbuster. In fact, it was better, because they could cache it at your ISP and caches always work better if people are boring and average. Worse, as the story goes, Netflix noticed a pattern: the more hours people watch, the less likely they are to cancel. (This makes sense: the more hours you spend on Netflix, the more you feel like you "need" it.) And with new people trying the service at a fixed or proportional rate, higher retention translates directly to faster growth. When I heard this was also when I learned the word "satisficing," which essentially means searching through sludge not for the best option, but for a good enough option. Nowadays Netflix isn't about finding the best movie, it's about satisficing. If it has the choice between an award-winning movie that you 80% might like or 20% might hate, and a mainstream movie that's 0% special but you 99% won't hate, it will recommend the second one every time. Outliers are bad for business. The thing is, you don't need a risky, privacy-invading profile to recommend a mainstream movie. Mainstream movies are specially designed to be inoffensive to just about everyone. My Netflix recommendations screen is no longer "Recommended for you," it's "New Releases," and then "Trending Now," and "Watch it again." As promised, Netflix paid out their $1 million prize to buy the winning recommendation algorithm, which was even better than their old one. But they didn't use it, they threw it away. Some very expensive A/B testers determined that this is what makes me watch the most hours of mindless TV. Their revenues keep going up. And they don't even need to invade my privacy to do it. Who am I to say they're wrong?

Netflix may be doing well in the sense that they have lots of revenue, but they're doing awful in justifying why they're worth the cost. I'm convinced that Netflix has less than 200 offerings in total, at least here in Canada. Most of those are going to be things that I would never want to watch, leaving me with 50 offerings. That's just not enough to justify using the service for more than a year, and I think a lot of people are starting to catch on to the fact that Netflix isn't what it used to be.

[0] https://apenwarr.ca/log/20190201