It sure seems that way. It's almost as if Netflix is giving in to pressure from content owners (which now includes themselves) to downplay or even weaken their suggestions.

First, they got rid of those wonderful ranked lists that made us love Netflix in the first place, replacing them with the much more opaque cover art carousel view. Then they started mixing in lower-ranked items into the carousel. Finally, they switched from the five-star rating to the thumbs up and down buttons, which can't possibly give them as much information about your opinion.

Currently I have zero trust in their rating system. Actually, zero is a bad number because if I see a high ranking I think I am going to dislike the suggestion. This is opposed to me previously trusting the system a lot.

I've heard rumours that the internal backlash at Netflix against 5 star ratings happened with Amy Schumer's most recent comedy special which had thousands of 1 star reviews on Netflix. Which was one of their most expensive comedy productions and whose release coincided not long before the switch over to vague thumbs up/down.

Note, the comedy special was similarly panned across the press and social media as being repetitive of her previous work, extremely predictable punchlines, and when seemingly good later exposed to be highly derivative of other comedians work.

But regardless of the reality/honesty of her rating it was apparently not good for Netflix's business when they let users destroy content they produced destroy it on their own web site via user generated content.

So the suits (heavily swayes by their production studio and Hollywood) were able to convince the product team to hurt the UX for 90% in order to protect the popularity of the 10% of content they own.

The truth may be good for consumers in almost all situations. But sadly the interests of executives dealing closely with high value B2B partners and investors tend to have a way to out-valuing the interests of the average user (not to mention far out valuing power users).

So I guess we have to rely on 3rd party IMDb web extensions which inject into Netflix in order to get honest ratings.

By "heard rumours" do you mean "read about it on Breitbart?".

http://www.breitbart.com/big-hollywood/2017/03/18/netflix-sc...

Notice that they're careful to say that they made the switch "amid" the special, not because of it. Also as far as I can tell, they have no actual data on the fact, and they're the only "newspaper" reporting it.

Nope... it was a Reddit self-post (incl. people who put together the viral compilation video of her joke 'borrowing', breaking down why she's no longer as popular, and analyzing Netflix's timing and stated rationale).

I'm curious why you brought up Breitbart? I just googled it and found a ton of other (non-political/right-wing) sites which drew the same exact conclusion between Schumer and the ending of 5 star reviews. Are you trying to say her thousands of awful reviews were somehow political? Or that it's all just some "alt-right" right-wing conspiracy?

- https://movieweb.com/netflix-cancels-5-star-rating-system/

- http://ew.com/tv/2017/03/16/netflix-star-ratings/

- http://collider.com/netflix-rating-system-thumbs-up/

- http://screenertv.com/television/goodbye-stars-hello-thumbs-...

- https://www.washingtontimes.com/news/2017/mar/17/netflix-cha...

I saw Amy's show live, which she later filmed, and it was just awful. Those reviews were highly justified. And I used to be a big fan of hers before anyone knew who she was.

If Amy Schumer wasn't the original reason why they started to ditch 5-stars then it was most certainly a motivating factor to get it pushed out (conveniently right after her special was famously destroyed - which got widespread press before the switch over to thumbs). As it most certainly also faced internal resistance as they were basically abandoning a decade of 5-star reviews via user-generated-content, in favour of a vague Thumbs Up/Down.

Amy provided a perfect example of the inconvenient and conflicting goals which Netflix has as a producer and content platform. The timing of the release would have been HIGHLY coincidental if it wasn't related in some fashion.

Outside of the reddit echo chamber, some people like Schumer, some don't, and most don't care.

Then I guess those 5-star ratings must have been some Trump supporter conspiracy brought to you by Breitbart and Steve Bannon then... not the fact she went off a cliff talent-wise when she moved to LA. Which was more than apparent to even non-fans of hers. And I say that as a big fan of her earlier work and as someone totally indifferent to US politics.

It's a hard problem but Netflix's model represents the state of the art in machine learning for recommendations. Still scared of the singularity? :)

There's a problem with all ML-based content recommendation, whether it's advertising, movies, whatever: the people who make the content have strong opinions about what should get shown to whom and when. If those people have negotiating leverage over your organization, your business team will compromise your recommendations to placate those people. That means that after you've build this beautiful model that minimizes whatever cost function you've chosen, there will be a bevy of business rules and content-specific score adjustments that will be overlaid. Over time, this shaggy, bad-assumption-laden system will dominate the user experience, and unless your management has a vested interest in maintaining the integrity of the recommendation system, your model will eventually be lost in a sea of human-generated noise. Not that I'm bitter about this or anything.

Personally, I used to get substantially better results. It would suggest to me a movie I'd never seen before with a score above 90, I'd watch, and enjoy. Now I'm getting things like kids shows suggested to me. I double checked by history to make sure no one watched one on my account, but they keep popping up with high percentage. I also really like horror and get weird suggestions [1] for similar movies. After I watched Alien it suggested the Great British Baking show. I literally have 0 trust in the new ranking system. They push certain shows way too hard and TV shows I'm watching show up on page 3 of "continue watching".

This is far from my previous stance of "Huh, movie I've never seen before and in a different language? It's 95% so yeah, I'll give it a go."

And before someone says anything, I do vote up and down quite frequently. But can't help but notice that my suggestions were better when I had the more nuanced star system.

[1] https://i.imgur.com/MOt3XlL.png

[1.1] How I'd rate these. Cars 3: not really interested. Liked the first though. Stitches: idk, doesn't look appealing. Teeth: Classic cult film but yeah... Big Mouth: I have ZERO interest in watching this, please stop suggesting. I have downvoted this! Waterboy: I like it, but far from 95%. I'll give it like an 80.

The coverage of the Netflix ratings format switch has always been frustrating to me because what people are complaining about (you're not the only one; https://www.polygon.com/2017/4/7/15212718/netflix-rating-sys...) was predictable from the beginning.

I do research in this area and it's fairly well established that when you go from something like five points to two points with ratings, you throw away tons of information. There's diminishing returns with numbers of points, but as you go lower you lose information.

The "ratings don't matter because what you want is implicit signals from peoples' actual behavior" is also disingenous because the rating behavior is a behavior that's directly tied to the stimulus in question. Not saying that indirect behavioral correlates aren't useful, only that the rating is a very powerful, direct correlate that tends to be very specific. Going back to the topic of the thread, sure, all those Facebook likes are going to be useful in predicting how much you like a candidate, but you're sure as hell going to get a lot of information by just asking them "on a scale of 1 to 5, how much do you approve of X?"

I mean it is pretty obvious that the loss of information is going to make it harder to predict. My big problem is that the system suggests completely off base content. Not to mention overly pushing their own content.

Since switching from 1-5 start ratings to like or dislike it's gotten much worse.

I honestly thought the 1-5 ratings were not useful since I either like or don't like movies, I am not interested in nuances. But, it's not working out as I expected.

Really? Because in terms of actual usefulness, I find youtube's suggestions to better...

Spotify is king IMO. Their 'discover weekly' playlist turns up gems every time.

But that's because they dive into other user playlists that contain the same music you play. Pretty simple. I assume Youtube do somthing similar.

If Netflix had playlists or a 'want to watch' feature I bet their recommendations would improve.

I tend to agree with you. The only thing I dislike about it is when I happen to open a link I'll randomly be sent or notice in an article that's something unlike what I normally enjoy or something I close right away and then for the next few days, or until I watch a bunch more of what I usually enjoy, the entire suggestion list is only things to do with that one random link.

I don't think it's possible on a phone, but in a browser you can remove videos from your watch history. Click the three dots next to the video for options, and select not interested. Google actually seems to take these into account, I had watched an Alex Jones video that was linked from elsewhere, and while it is good to not have too much of a bubble there's just certain things I don't need to see more of.

Or, just use a separate browser. I typically only use opera for YouTube & anything where I don't mind Google tracking me, but if I open a YouTube link in Firefox I'm not logged in (and w/ opera's VPN enabled it doesn't appear to affect YouTube recommendations). I'm sure Google does correlate traffic between the two to some extent, but this seems like the only useful way to use operas integrated VPN.

Youtube does better than Netflix for me, but they still suggest stuff that I have already watched. Sometimes stuff I literally watched an hour ago. And I watched one Joe Rogan episode and it will not stop suggesting it to me but fails to notify me on things that I watch every episode on; like 3Blue1Brown, Robert Miles, or Rare Earth.

There’s a bug confound with the catalog: Netflix’s streaming catalog is much smaller than their DVD selection was and it changes as licensing deals expire. No model can make up for that entirely.

I use the DVD one and I do find the suggestions are mostly pretty reasonable ones.

How hard can it be? Any film that I didn't watch all the way to end ought to be a strong signal that I didn't enjoy it. Not that I want to watch other movies just like it.

OTOH, that you even chose to watch a movie is a sign that you are interested in the genre. Just because I abruptly stopped watching “Star Trek IX”, doesn’t mean that I have abandoned the sci-fi genre or even Star Trek

I have a friend who shares my account since I stay with him when I am in the UK. It isn't worth the effort to have two profiles.

So, he likes horror movies and will watch any horror regardless of any signal that it's going to be poor. You can look at the viewing history and see he rarely goes beyond 5 minutes of watching any of them.

I watch Netflix regularly throughout the year, he watches in phases that last a week or two and then nothing at all for months at a time (for reasons that should seem obvious by now).

As a non-horror movie aficionado, I can say with certainty that only 2% of horror movies are ever worth watching and only 50% of these are any good. As a consequence, my personal viewing history includes almost no movies in this genre.

My favoured genre is drama and I normally watch all the way through.

You should be able to guess by now that I should rarely be recommended horror movies but, alas, Netflix thinks otherwise.

Btw. I also rate movies I watch - my friend doesn't.

I’m confused — you honestly expect Netflix’s model to have figured out that your profile is actually 2 people based on what you believe to be regular and obvious cyclical patterns? I would have to imagine that this is a relative edge case for Netflix, and there is no obvious answer for what to do with someone who mostly likes drama but for some reason, goes on a periodic horror binge.

I assume that Netflix’s model has the premise that profiles aren’t in fact, very easy to create. I have separate profiles for my parents, as well as a test profile to see what happens when a user only seems to like the “Human Centipede” trilogy.

2 people but only one consistent regular user. My recommendations are heavily biased towards the occasional user - who is also giving very strong negative feedback?

It doesn't bother me since I know what I like. There are not that many good films that I'm not going to find them anyway.

Someone, or a group of people, are being paid for nothing though. I don't know anyone who subscribes to Netflix because of their recommendation algorithm.

> as a test profile to see what happens when a user only seems to like the “Human Centipede” trilogy.

And? You can't just leave us hanging on that.

After creating the account and immediately giving a thumbs up to the trilogy, I've only occasionally logged in and feigned "interest" by clicking on the movies as if I'm about to watch them, or that I enjoy re-reading the synopses. The recommendations are all normal and not noticeably feces-related. But maybe I haven't yet met the threshold for the model to consider me a particularly engaged (or real) user.

I feel like watching 20 minutes of a movie and then downvoting should be a strong indicator. This does not appear to be the case. In fact, it seems to be more likely to appear as the first suggestion when I do this.

Yes, it's a hard problem, but no, Netflix recommendations are nowhere near state of the art. They were at the time of Netflix prize, but since then the field has advanced a lot, while Netflix only dumbed down their recommendation models.

In my anecdotal experience, it seems like Netflix's own content gets weighed disproportionately high. This could be a result of the fact that first-party content never gets removed, so it builds up a more complex graph of recommendations, but personally it feels like they're trying way too hard to cram their content down my throat -- to a point of recommending shows that I would never even consider watching, like Jane the Virgin (not exactly my kind of humor). In the end it just feels like they've compromised their own recommendations to push their own product, which makes me mistrust all of their recommendations.

Are you saying all of Netflix suggestions are getting worse? How are you measuring that?

Or are you saying your netflix suggestions are getting worse?

Mine are pretty good and have held steady for a while, at least in terms of my own preferences, though I also am probably using it a little less as I've got Prime and Hulu now as well, so there's probably less times I'm randomly searching through Netflix and finding nothing.

I can only tell you what my personal experience is. I laid it out more in another comment under rjurney's reply.

I'll also add that I am more frequently searching for 15 minutes then switching to another service. I used to find a movie to watch in 5 minutes. I am a big movie person too, and will watch most things. But I am also more aware that if I watch one show that is just "meh", then I am going to be bombarded with shows of similar quality for the next few weeks.

Also, there is a fairly obvious pattern that shows up from the movies in "my list". Those do not seem to be weighted more heavily.

It is surprising how bad the recommendations can be sometimes though – I avoid watching comedy specials on Netflix because if I watch one, then suddenly I must want to watch every single comedian ever. It's reminiscent of buying something on amazon, then seeing tons of retargeting ads for the same thing. Oh well, like I said, in general, diversifying my streaming sources has helped a lot.