[hobofan]

> but in reality they just couldn’t handle the data

If you mean from a "critique" standpoint then no. If you meant from a "data wrangling standpoint then yes, as binary like/dislike data is just a lot easier to work with for recommendation algorithms.

There are many good reasons to move from a five star rating system to a binary rating system, and yeah, I think many of those fall into the "people lied to themselves" category:

- People tend to note use five or ten star systems on a continuous scale with certain points on the scale being biased

- People tend to go into "movie critic"-mode when they see a 5/10-star scale, as those are usually used on sites like IMDB. That drives them to try and rate the movie "objectively" and in accordance with an intellectual image they want to portray, rather than what they actually like/dislike consuming and spend their time watching

- Netflix also displayed the ratings as 5-star "adjusted ratings for the viewer", which already took your preferences into account. Not a single person I've talked to back then was aware of that, so everyone tried to do the same mental gymnastics they do when trying to project global IMDB ratings to their personal preferences. Moving to a "XX% match for you" together with the like/dislikes is something that people understand a lot better

All-in-all, I don't think the rating system really has been an issue in the recent years. The catalog has been a much bigger issue during that time. I'm pretty sure that Netflix's rating and recommendation system has been good enough that it has served me everything that I'd like to watch on their platform and now I'm out of content.

[danielscrubs]

So to summarize, customers are dumb & recommendation algorithms are hard. Conflating time watched with user enjoyment is much easier.

Sounds pretty similar to all mediocre companies out there.

I loved Netflix stuff when they started out, banger after banger was delivered. Something changed.

[hobofan]

> So to summarize, customers are dumb & recommendation algorithms are hard.

Or to not throw away all the points I argued for: There were a lot of good reasons for moving away from the five star systems, and the main motivation was providing a better less confusing UX

> I loved Netflix stuff when they started out, banger after banger was delivered. Something changed.

The main thing that changed was that media companies woke up to streaming and stopped giving away the rights to properties that many people enjoy (mainly nostalgia shows + blockbusters) for cheap. That forced Netflix much more into media production of their own, which has it's up- and downsides, and gave Netflix the same warts that media companies always had (e.g. having to make hard decisions around canceling shows). It's a very clear case of a first-mover losing its advantage over time.

[watwut]

> Netflix also displayed the ratings as 5-star "adjusted ratings for the viewer", which already took your preferences into account.

It was not adjusted to your preferences. It was adjusted by algorithm to ... something that has nothing to do with my enjoyment.

[nomel]

Netflix, famously, had a public contents to try to improve the recommendation system, with very little success. [1] That system was a demonstrably good system, that maximizes preference, but only if you rate a bunch of content. I'm not aware if it's still in use.

But, I've rated many hundreds of shows/movies, and the rating is very accurate, for me. Very biased and scaled, but completely predictable/reliable.

In my case, the rating can be corrected with Netflix 7 being my 0, and Netflix 10 being my 10, mostly linear. For a Netflix show, 8 is my 0, since those are biased a bit more.

[1] https://en.wikipedia.org/wiki/Netflix_Prize

[nomel]

0 means it wasn't worth the time. Negative means I disliked it.