And they don’t ask because users don’t provide useful answers.
But users don’t provide useful answers, because rating things doesn’t do anyone any good.
I’m of the belief that if you can make ratings useful (catalogue all movies, including not on Netflix; give useful ways to view/update your lists; have direct relationships to recommendations), you would have dramatically better recommendations for dramatically less effort/complexity.
I don’t think you’ll ever get to “good” recommendations based on usage. The data is fundamentally garbage.
Of course, the other side is that Netflix isn’t interested in recommending things I like; their goal is to recommend things I’ll put up with. They just need 1 show worth watching and subscribing for every now and then, and N shows to keep me mildly amused to stop me from dropping it between good ones
By analogy, Netflix went from being a sci-fi future of having and being able to recommend on the basis of _everything_, to having a handful of good offerings and a huge amount of b-movie-level offerings.
My gut sense is management tried to paper over this "content loss problem" by making changes:
1) to the recommendation system to push Netflix content[1]; and
2) making changes to the UI to force users to be more reliant on the recommendation system.
I suspect these changes have, generally speaking, made user-consumption metrics look decent--in my mind the core of almost all Netflix's post-streaming decisions. But, as you suggest, it is all papering over a problem of user dissatisfaction: Netflix recommends you mediocre content, and you eventually give up and watch it--and then feel meh.
[1] I can imagine Netflix executives being unwilling to report that the content Netflix had paid mightily for scored low on Netflix's own recommendation algorithm. Philosophically, Netflix went from being, essentially, content agnostic (e.g., it just bought more of X DVD), to having incentives to see particular content (e.g., its own) rank highly.
The recommendations were pretty good, because I remember we mostly picked what was recommended.
Now a days, I'm certain Netflix recommends content to feature either "no cost" (owned) or the content with the lowest licensing fee. I don't believe for a second they don't have the data suggest the best movie. They simply don't want to suggest the best movie. As you said, their goal (now) isn't to suggest the content the user is likely to enjoy most, it's to suggest content the user will tolerate. And that's exactly why they shifted away from a 5 star rating system, to a thumbs up/down approach... even if you didn't love a movie or show, you're still likely to give it a thumbs up unless it was totally awful.
Large numbers of books labelled as 'free with your membership', which likely only cost Amazon the price of delivering the files. Which makes sense, because once I have paid for my credit the worst outcome financially is that I use it.
I'm certain Netflix ran the numbers, and determined that a high-usage customer is the most valuable.
On "just ok" vs stuff actually enjoyable, "just ok" is fine until there is no better competitor for attention (e.g. a new smartphone game takes over the world). If they get to fit on the "actually enjoyable" scale instead, there is a better chance for people to keep their subscription, sometimes even if they end not viewing anything that month for whatever reason.
[1] https://www.independent.co.uk/arts-entertainment/tv/news/net...
- lots of short content
- viewing metrics to the second
Within an hour of usage you could've browsed through hundreds of TikToks, and allowed them to classify many tastes for you.
You'd need to sit in front of Netflix for an entire month for them to get the same amount of signal.
What you need is sufficient reason to do so — the values need to actually be useful to you to make updating an act of sanity (unlike now, where it’s purely an act of futility). Feeding the algorithm is not itself sufficient (though necessary, and currently ineffective). The ideal recommendation system would encourage rating entry as a ritual act, and more importantly, rating updates an act that derives real value.
Only then will you have good data, and from good data, a dumb algorithm will suffice.
And then the realization that really the best recommendation isn’t to forge a new customized list altogether — it’s to simply find the most similar users and recommend items from their list. (MAL has/had a cosine similarity function for this, but no way to search because it’s basically an n^2 algorithm on 4M users; apparently they offered it at some point, and quickly found it untenable. That was what really kicked me off)
And then the realization that if I found users with similar taste, then shouldn’t they be friends? So then it becomes a MAL friendship algorithm..
Did a bunch of research on recommendation algorithms and weighting strategies, scraped most of the MAL users, stored it in a database, and then promptly procrastinated on actually implementing the algorithms. Been sitting on that for like 3 years now :|
[0] https://www.amazon.com/Otaku-Database-Animals-Hiroki-Azuma/d...
It’s correct from Netflix’s perspective, but not from mine.
