Netflix Replacing Star Ratings With Thumbs Ups and Thumbs Down
variety.com
variety.com
This saddens me.
>Users would rate documentaries with 5 stars, and silly movies with just 3 stars, but still watch silly movies more often than those high-rated documentaries
That's not incongruous to me. The stars are not about "enjoyment" factor, they are about perceived quality. I may have a go to cheap ice-cream and rate it 3-stars but rate a good affogato 5-stars and only have it once in a while.
They are diluting the meaning of quality and instead are opting for a saccharine "enjoyment" factor. This binary choice does not sit well with me and I hope they abandon the idea soon.
A movie like "Suicide Squad" was (to me at least) highly enjoyable, though I thought it objectively had many problems with plot and delivery. I would probably give it 3 stars, which is relatively neutral for me (or leave it altogether unrated). If it were thumbs up/down, I would probably have to leave it unrated completely.
Maybe that's an improvement, but the more discussion I see around this, the more I realize the futility of single-axis metrics for things like this, until and unless you're asking a single question "Did you enjoy this movie" is a much different question from "is this a good movie" -- and I don't really know which one I've been answering on Netflix all these years past.
3 stars = "not a bad movie, but wouldn't recommend"
4 starts = "decent movie, would recommend"
5 stars = "great movie, would definitely recommend"
A binary choice doesn't have any weight to it.
This is actually rude of you, bordering on ridicule. He explained very clearly why he is dissatisfied.
I'm worried about the thumbs up/down, as Netflix has increasingly recommended shows I do not like this year, and I don't think the thumbs solves the "rating for perceived quality" issue. I've been using Movielens for recommendations a lot instead, and it seems more accurate for me... and it has enough stats to show that on their site, most rating is not binary, much closer to a normal curve for average movies or a linear graph on really good movies. (It lets you see the distribution curve on individual movies, and other metrics.)
I thought that Neflix made it pretty clear that the ratings were used to choose which content to show me. So I should only rate based on what I'd actually like to see.
They are just trying to increase number of views for a poor quality library. This will mask how bad most of the content is and increase their numbers.
My dream is to build an open source app that gathers all these personal ratings (from Netflix to Pandora to GoodReads to restaurants) and puts it back on where it belongs: the users hand!!
Unfortunately finding the time to work on this will be tricky..
Anyone knows if that's a thing already?
Though quite a bit less than they used to. My observation is that more and more back catalog type of items are no longer available.
Or is that my wishful thinking?
Of course, given that they pretty much put every other rental place out of business, your only choice in a lot of cases is just to buy a disk if you want to watch something that isn't available streaming.
I've looked before halfheartedly for a way to export them but now that they cut the API off it doesn't seem like there's a great way to do that.
[0] https://www.reddit.com/r/netflix/comments/5zzc4g/batch_downl...
ratings = [];
jQuery('li.retableRow').each(function (i, row) {
ratings.push({
id: jQuery(row).find('.title a').attr('href').replace('/title/', ''),
title: jQuery(row).find('.title a').text(),
rating: jQuery(row).find('.starbar .personal').length
})
});
JSON.stringify(ratings)
ratings = '';
jQuery('li.retableRow').each(function (i, row) {
ratings +=
jQuery(row).find('.date').text() + ',' +
jQuery(row).find('.title a').attr('href').replace('/title/', '') + ',' +
'"' + jQuery(row).find('.title a').text().replace('"', '""') + '",' +
jQuery(row).find('.starbar .personal').length + "\n";
});
Edit: Giving away one of my 48h Crunchyroll premium guest pass. No credit card required. I'm not working for Crunchyroll in any way, they give out guest pass monthly to premium users. First one to use this code wins: UWWJAQTDKYZ. Note that I will receive a notification telling me your username and perhaps your email address, if privacy is important to you.
At that point, isn't just plain old piracy more convenient ?
I used to use a VPN to watch US Netflix, but they started to block known VPNs and I didn't feel like spending time on workarounds. On the other hand, downloading the latest episode of some TV series takes only a few mouse clicks. For sure easier than fighting geo-restrictions.
I still pay for Netflix, but doesn't really watch it much because of the tiny catalog available to me locally, I simply want to support them. Perhaps it will make me feel better if I would download any of their content. :)
>I simply want to support them.
Nothing to be sad about. Also curious, why do you want to support them ? Being a market leader, Netflix is a unique position to influence some of these regressive practices. More support is clearly not helping; it's only making it worse by creating monopolies that exert even more negative influence.
Netflix is not much of a market leader until it contains at least 90% of the movies i want to watch, which can't happen without enough leverage from their part given by user support.
But ofc, I wouldn't mind if they had some real competition, I don't want a monopoly.
Geo-restrictions have existed forever. What Netflix started was actively blocking entire ranges of IPs belonging to vpns and commercial ISPs (cloud providers like aws, dedicated servers like ovh etc.). This should fall under some corollary to net-neutrality where a service cannot discriminate between ISPs. Does such a corollary exist in countries that have some form of net neutrality (US, India, Netherlands etc.) ? Can someone with legal expertise comment ? From my standpoint, they can have geo-licensing, but should stick with some consistent way of showing content either based on billing info or IP. Blocking out vast portions of the internet because it doesn't suit them should clearly be some violation of laws like net neutrality.
I'm on the brink of cancelling my membership as it is, this certainly won't help, though it's obviously the proverbial straw rather than a big deal in its own right.
How do you compare ice-cream with cheesecake?
I ignore ratings because of that. They don't mean anything and I am guilty.
On Good Reads, I have given what is deemed as a trashy piece of writing 5 stars. At the same time, I have given Dune 1 star because the story was garbage.
So what is this 5 star rating? Some people say it is the experience, some say it is the writing style, others say it is the story telling, or the plot.
Lets also face it, when it comes to films, the news papers have their own agendas and personal commitments to other agencies. My film could be absolutely garbage, but since I dined with the Editor a few times, It gets 4/5 stars and then, it gets 4/5 stars because the rest of the population is told how to feel.
I would even say that this whole system of rating based on Stars or Likes can simply be removed. If a film is good, you will know about it. If a film has been watched 1000000 times more this weekend than another, base it on that if anything.
And the rub is that not everybody uses the same projection, which means the utility of one person's rating for another unknown their projection system (the weight they give to each sub-component of the overall rating) is completely unknown.
For all the faults of the old system, the new one is even worse. Like Pandora, I'll probably just never rate things or mostly just give them a down vote if they don't crack a threshold of goodness.
They should be optimizing "number of people who pay for Netflix". Instead they are optimizing "number of hours spent watching Netflix".
Those two metrics are correlated, but definitely not fully.
If I spend hundreds of hours a month watching meaningless action movies and TV on Netflix, I can cancel my subscription and save myself hundreds of hours and $10. I've got lots of other ways of filling my time with endless pablum.
If I spend 2 hours watching a thought-provoking documentary, I say to myself "yup, that justified spending $10 on Netflix this month."
> Users would rate documentaries with 5 stars, and silly movies with just 3 stars, but still watch silly movies more often than those high-rated documentaries.
Speaking anecdotally, the one (temporary) time I canceled was when I found myself in the paying-but-not-using category.
I hope their version of that signal is better than what they're presenting in the UI. I have a few movies Netflix shows me as not finished, becaue I closed them just as the ending credits started.
Upon finishing the final season of Dexter, I clicked on an episode in the first season to look up something. I jumped to the 18 minute mark and watched about 10 seconds and closed the tab.
Now Netflix asks me every time if I want to continue watching Dexter from where it now thinks I left off in season one even though I spent a whole year watching all of the seasons.
So I have the same concern. If they have a more sophisticated model, it certainly doesn't get reflected in the UI...
And they force 21:9 movies to be played back inside the 16:9 portion of a 21:9 ultrawide monitor, with massive black borders as a result where there should be none.
I'm seriously unimpressed with Netflix as a company, for what they do with all their resources. Sometimes the MVP just isn't good enough.
For you. But are you representative of the whole or even a majority? I personally use it to reflect whether I enjoyed it or considered it a waste of time.
I give some of my favorites - like Hackers - 5 stars but would never consider it a "quality" movie. It's awful and silly but that's the fun of it.
Ultimately the key reasons to have these ratings in the first place is not to let people express their feelings, it is to get data for the recommendation modeling to predict better. Perhaps showing a simple like/don't like increases the number of people rating something which gives you more training data and thus better business outcome from your model without compromising on midel quality since you had to anyway model it bi-modally before.
There may be reviewers who do not generally fit the bimodal distribution, but if they are the minority, why design your whole rating system around them? Better to optimize your system for the way most of your users behave.
The reason online ratings tend to be bimodal is because the respondents are self selecting sample rather than randomized. Since product ratings are not typically compulsory, the extreme likers/dis-likers tend to fill the survey question.
When implemented as compulsory, it drives poor user experience and having to fill a survey to get something anyway introduces a bias (just fill something to get it over with). So no easy answer.
This is also the reason traditional survey firms haven't really gone out of business even with the wealth of online data from Facebook, Twitter and product reviews available, though better/easier access to actual behavioral signals are certainly replacing surveys in some areas.
Just curious if I am an outlier with how I do my ratings.
1 - Terrible. Good Lord, who thought making this was a good idea?
2 - Poor. Would not watch again
3 - Fair. Neither for or against. Unlikely to watch again, but I don't grudge the time spent.
4 - Good. I enjoyed it. I would probably watch again.
5 - Exceptional. Probably in my top 10 now, and foresee enjoying watching for years to come.
3 and 4 are used a lot. 2 is used occasionally. 1 and 5 are rare.
1 - Did not like it
2 - It was ok
3 - Liked it
4 - Really liked it
5 - It was amazing
The reason being is that most books won't end up being 1 or 2, and there really isn't much difference between them. But it sucks giving 2 stars to something that wasn't bad. Perhaps emoji arranged in a non-star-like pattern would be the best.
1) How good was the movie? [1-5]
2) How likely are you to watch it again? [1-5]
I think it was too many questions to ask, but trying to separate out the enjoyment from the quality struck me as a good idea.
Airbnb does this pretty much spot on, in that they ask for 1-5 stars for five categories, rather than just "did you enjoy this Airbnb?" I also think that Untappd does this wrong, because in general beers can't be described in just a 1-5 star rating without deeper explanation.
I think that moving from a pure star rating to a thumbs up and down rating is better overall, if only because it makes me, as a watcher, not have to think as much and therefore give a rating where I might not have before. If I want to go more in depth, I can explain more too.
My favorite example is Fallout 4. There are tons of negative reviews from people who put 600+ hours into the game. How the hell do you put 25 days of your life into a game you don't like? They claim it "got boring". Not many games will not be boring after 600 hours...
This is only true when those star ratings are taken out of context. Individual reviewers can have completely coherent star ratings that are very useful for making determinations.
The golden age of Netflix discoverability, for me, was back when they added social networking features. I connected my account with many friends and family and was really able to get a sense for everyone's taste in movies. When it came time to discover new movies/tv to watch, I'd open up the ratings history for someone who's tastes I felt aligned with the mood that I was in. Some of my friends would only rate high quality content with 5 stars, others would typically rate mass market, mindless entertainment with 5 stars and others tended to include a lot more 5-star reviews within a certain genre. Over time, I got a feel for that and could use it to my advantage.
But when you remove that individual context and only aggregate 5 star reviews across a massive population who all view the 5-star scale in their own way, you get that uselessness that you talked about. Some people may reserve 5-star ratings for truly extraordinary content, but Netflix doesn't weight those ratings any more highly than the 5-star ratings of someone who rates nearly everything as 5 stars. This makes the ratings that Netflix displays meaningless. But that's only because Netflix's current methodology makes them meaningless, not because they're inherently meaningless.
Is that fact or speculation? It's a common recommendation technique to find the mean rating for a person, and use that to normalize. Normalized score = (actual_score - mean_score + 2.5) for a 5-point scale, for example.
I have no insight into whether or not Netflix does something similar, but I'd be surprised if they don't.
Imagine two people watched 100 movies, and assessed them the same way, say that 20 are five-star, 20 are four-star, 20 are three-star, and so on. The first person rates all 100 movies, but the second one has a habit of writing a review only if he considers a movie excellent. Then your algorithm will wrongly discount the second person's ratings.
Is this true in the real world? Have you found different people to have different habits, like rating everything vs rating only good movies vs rating only bad movies?
Second, I don't know whether individual ratings should be normalised as you say, but the final rating that's displayed should be normalised. For example, the app stores should have 20% of apps rated five stars, 20% four stars, and so on. This will make the ratings more objective and easy to understand. If the average rating is 4, then an an app rated 4 is merely average, which is counter-intuitive.
(Perhaps normalised within a top-level category, like Productivity and Games)
An exquisitely detailed system like Airbnb's costs the user more time & hassle. Which means, assuming you make it voluntary, that a lot of them skip it. Meanwhile a simple/easy system like this, turns a scalpel into a sledgehammer somewhat, but you get a lot of them. So there's a balance to be struck for your site's particular userbase.
The nuance comes when you aggregate many people's binary decisions. A value near 1 tells you many people felt the good way outweighed the bad; a value near 0 tells you the opposite; and a value near 0.5 tells you that people are split. I think that's far more useful than trying to ask many individuals to provide you with a granular score.
For example, I found Mirror Mask enjoyable and would recommend it. They dreamed up and brought to life a fantastical world full of magic and wonder. The visuals are excellent. The story is mediocre, so you should watch it with the right expectations, but watch it nevertheless.
The ability to distill something complex and multi-dimensional to a clear conclusion ("enjoyable and I recommend it" in the above paragraph) is a sign of clarity of thinking and expression. If you can't do that, I'll go by other people's ratings.
Remember that the goal isn't to accurately capture one person's opinion, but to help potential viewers decide whether to watch it.
Wouldn't it be more beneficial to have fewer people give a more detailed review to describe what they like and dislike about something? What use is "good" or "meh" compared to for example "I rate this product low because the shoes are very narrow for my normal feet" so you can actual relate it to what you actually are looking for? One person's "meh" could be another's "good"...
From the article:
"This makes sense – giving a five-star rating takes some thought, especially for something like a movie or TV show.
A binary “yes or no” option is much easier for viewers to commit to, [...]
[1] https://youtube.googleblog.com/2009/09/five-stars-dominate-r...
A binary option actually has three options. Did it prompt me to rate it as bad, or did it prompt me to rate it as good, or was it bland enough where I don't want to sway the rating of it at all?
If I rate a movie on IMDB as 5-6, and maybe even 7, the possibility of me recommending it to someone "depends". In that case I might as well not rate it at all.
Ratings are inherently tricky, because we all like different things. I don't think you lose anything by going to a like/dislike system. Recommendations should be based on aggregate maps and matching you with similar viewers that have a certain overlap in ratings. That's made easier with binary choices.
IMDB ratings are only partially informative to me anyways.
Now, I realize that's almost certainly not the level of individualization offered by their ranking algorithm (I'm convinced there's way more of a "groupthink" dynamic), but that's partly my point: if your ratings and recommendation system isn't capable of supporting individual differences, then your algorithm is broken.
If you have a complex problem (personalized recommendations absolutely are complicated) then you shouldn't fix a broken algorithm (Netflix recommendations are, at least for me, a bit meh) by simplifying the inputs (switching from stars to up/down). You should fix the broken algorithm, by creating a better algorithm.
I realize it isn't that simple, particularly for a publicly-traded company whose stock price would absolutely be influenced by a decision to scrap part of their core technology, but from my perspective as a user, this is going to hurt my experience with the product.
But the signal isn't just for Netflix; it is also for users, who might sometimes be in the mood for something silly and sometimes in the mood for something good. Also, people might rather get more suggestions of good movies even if they are more likely to watch bad ones. (Of course, people might also just overrate documentaries.)
I'm going to rely on Netflix's recommendation engine to put things in front of me that I am going to enjoy.
Why not remove recommendations altogether? They clearly don't have a library to support it.
I'm reminded of Jerry Garcia's famous quote about the Grateful Dead: "We're like licorice. A lot of people don't like licorice - but the people who like licorice REALLY like licorice".
A 3.5 could either mean 50% of users give it a 3, and 50% give it a 4, or it could mean that, say, 30% of users give it a 5 and 70% give it a 3 or less. How do you know you aren't part of the 30%? Great art is often polarizing.
If they remove the axis of quality and merely opt for "will you watch this: yes/no", it will make it harder to select "good popcorn trash" versus "high-quality, high-enjoyment" to fit my mood.
Star-rating can be too much detail, anyway: if you're comparing two shows, and one has a higher fraction of five-star ratings, but the other has a higher fraction of ratings that are four or above, which is better? Star ratings can be too much detail and cause confusion.
If you want more detail from each person, you can ask specific questions with a yes/no answer, like, "Were parts of it boring?" or "Was it violent?" That's probably better than star ratings.
The iOS App Store is a good example of this. An unbelievable number of people, for some reason, thing 1 star = great and 5 star = bad.
The other thing that annoys me with ratings in general, specifically places like amazon, is when people pan a perfectly good item because of some third party factor.
"Item was great, but it came in a beat up box from the post office, I'm giving it one star."
WTH does someone drop kicking your box have to do with the product?
Bad reviewers will always be part of the system. You have to expect it. Netflix has tons of reviews that go "I turned this off before the opening credit sequence finished, worst movie ever!!!! -- 1 star".
3.5: Good place, check it out
4.0: Great place, impress your friends
< 3.3: Double check your health insurance first
> 4.2: Fake
For example, you can look at the 50 Shades films or A Dog's Purpose or (especially) Gunday and have a pretty good idea that lots people are giving them bad ratings without having seen the films. (Their weighted average ratings are supposed to combat that, but they don't seem to be doing a very good job.)
(Obviously, "collect a dataset that philh finds interesting" should not be a business goal of Netflix. I'm not suggesting this is a reason to keep the star ratings. It's just a thing that I like about having ratings more detailed than yes-no.)
One good way to accomplish this is to pool users into cohorts of similar tastes, then suggest movies enjoyed by their cohort but not yet watched by a given user.
The question is whether doubling the frequency of rating a title is more valuable than a slightly more nuanced understanding of what each person likes. 'More valuable' meaning they can build you a better cohort from which to make title suggestions.
If you decide you want a more nuanced understanding of what a given reviewer likes, I think it's better to evaluate different dimensions (Was it funny? Was it violent? etc) than shades of grey* of the same one.
* pun not intended :)
5-star rating systems are broken. A 1/2/3-star rating is effectively a dislike, and a 4/5-rating is effectively a like. In the big data sense, a deviation from a 1-star for dislike and 5-star for like is statistically meaningless. (and this is universal; star ratings on Amazon Products and Yelp Locations have the exact same distribution).
The interesting part of this is what Netflix gains from the change, since their recommendation algorithms will become less granular. Maybe they came to the same conclusion?
Source?
http://www.lifewithalacrity.com/2006/08/using_5star_rat.html
At any rate I thought it was common knowledge that 5-point ratings have issues - especially as a "social rating system"?
it's sad teenagers who think life is black and white are the ones deciding it for others
luckily there is TMDb where i have my watchlist, imported ratings from IMDb and can discuss there each individual movie
That's the other problem: since the breakdowns of ratings are skewed, the average movie (the arithmetic mean) tends to have a rating of ~4 stars. Another reason to get rid of the system.
But that's not even close to true. In addition to it technically being up/down/no rating, we've got how long people watch the show for, how the shows they watch form a pattern, how all the patterns of what people watch show global preference patterns, whether they rewatch a show, when they watch what sort of shows, what individual scenes are rewatched vs. skipped... Netflix is swimming in a sea of preference data, not sitting here trying to figure out "Gosh, um, if the user likes this movie 3 vs. 4 stars, uh... what does that mean?"
It makes perfect sense to me to optimize this one-data-stream-among-many to increase user participation and get more bits of information from more people engaging with the simpler system, rather than trying to squeeze bits out of the few people willing to use the star system and the even fewer willing to write useful reviews.
It isn't really even as shocking as it may seem at first. The star system has 6 states, "no rating", 1, 2, 3, 4, and 5 stars. That's 2.6 bits, with some simplifying assumptions [1]. The thumb system has up, down, and no rating; that's 1.6 bits [1]. To make up for the bits, you need only see ~40% increase in participation over the current star system... think that's going to happen?
[1]: The simplifying assumption is that all outcomes are equally likely, but that's not true. I don't have the numbers to run a more complete information theory analysis, but it's not hard to imagine the "no star rating" case is so common that it produces such a small fractional bit that a higher-participation-rate yea/nay/no rating (if you puth this UI in their face, "no rating" becomes much more meaningful, too) straight-up produces more bits of information on average, and is thus simply an improvement even before considering the superior UI experience. I rather suspect this is the case, some very sensible assumptions would suggest this, but I lack the ability to prove this; the "assume all outcomes are equally likely" is at least a concrete case I can discuss.
This move seems very inward-focused: Netflix is thinking about the information it wants to get from users, but I wonder if they surveyed people about how much they want to see percentage-likes instead of stars.
Of course, Netflix solves this by removing the quality content....
For some people, such as myself, it will decrease participation (making the "no rating" indistinguishable from "it was okay, but I don't love it and don't hate it, so I wouldn't know whether to upvote or downvote"). A 40% increase is also quite a significant change. I'd be astonished if they got that much more engagement out of it.
There's also quite a difference between having the data for their internal algorithms and what people want to see. Just like Facebook eventually added alternatives for just the "Like" button.
I don't mind having a percentage displayed rather than a star rating (I automatically convert everything in my mind anyway, even though the star ratings are more easily visible at a glance when scrolling through a list), but I mourn losing a lot of expressiveness in the ability to record what I really thought of the video that I watched.
I don't understand why engagement is the right metric here. If someone isn't sure how they feel about a movie, why is it a benefit to have them spew their half-formed thoughts into a like/dislike rating?
many has more value than
thougthgful one from few
Seriously. Lots of low fidelity measurement is sometimes better than a smaller amount of higher fidelity measurement. Especially when that higher fidelity measurement has errors.
The goal isn't making a database of the "best" and "worst" movies and TV shows. We already have the International Movie Database for that. This is just about finding out what you like, and finding other stuff like it.
That's pretty harsh.
I've watched plenty of movies that I enjoyed and would give a thumbs up but aren't sure whether it should be a 3 or 4 star movie. There's movies I love but consider too flawed to give 5 stars. And I find little difference between 1 and 2 stars other than the level of regret for wasting my time.
Overall I rarely bother rating things on Netflix because it just doesn't feel worth it. So they get nothing out of me, whereas with a thumbs up or down I am more likely to chime in.
You could say "well that's the same as not rating it!" but it's not. I'd like there to be a clear distinction between "I abstain from voting" and "I thought this content neither vomit-inducing nor life-changing."
I no longer see a sum of my ratings but I believe it is well over 2,500. I know others that have rated a lot more (when there was the friends feature you could see your friends' number of ratings). Because of my number of ratings I felt that Netflix did a pretty good job with recommending me content. I will be sad to see this go as I definitely refer to star ratings when adding content to my queue.
Others have mentioned that the two factors of quality and enjoyment make the star rating more valuable and I agree. The only time I remember it breaking down for me was the film Rachel Getting Married (though I am sure there were others). I couldn't stand Anne Hathaway's character to the point that I gave it one star but at the same time I recognized that she gave a really strong performance in what was probably a good film.
Are they converting ratings to thumbs up/down? What does a three-star rating convert to? Those are typically movies that I enjoyed but wouldn't rewatch or recommend to others.
It would convert to no rating.
Meanwhile I rate everything 1-star vs 5-star.
So I'm not too convinced there's any nuance in star averages, much less a difference between "decent" and "fantastic". It might just be a more divisive movie that averages out to a 3-4.
Rating is a hard problem. No system works universally well. IMDb (Amazon property) for example uses 10 stars, Amazon uses five stars. Facebook use thumb up ("likes"). Games ratings are often in percent 1-100% (summarized by metacritics.com and others). School systems around the world use A, B, etc or numbers like 1-5 or 1-6 for grades.
You should also distinguish between aggregate ratings and individual ratings. Metacritic and other aggregators normalize to some scale, but not all the individual reviews use the normalized granularity. A common movie case is Metacritic having to normalize a 5-star, in half-star increments, review to a 100-point scale. I believe Metacritic also allows user reviews to use a 10-point scale, but present the average on a 100-point scale (the first ten integers but with one decimal point, technically allowing 101 ratings from 0.0 to 10.0). Aggregate ratings always have more detail than their constituent parts.
Meanwhile, I have to like all the things the same amount. This is just going to lead to me not rating the vast majority of mediocre content on the platform.
Why not let you choose as a user?
I'd rather have seen the money wasted on her spent boosting the publicity of female comics that are actually, you know, talented. Start with Tig Notaro.
Perhaps they could bring back a "critical review" mechanism, but I'd guess it wouldn't affect your matches at all. And probably be mostly unusable on a TV anyway.
I think Netflix's move might help this. It certainly lowers the cost rating.
Even most of my disappointments tend to be in the vein of "It was OK I guess but I don't see why people think this is so great."
I bet many new users don't even know they can rate. I wonder if people aren't rating much because the UX sucks, not because it's a 5-star system.
I'm about ready to drop them, just like they've dropped MASH, soon to be X-Files, and many many movies.
- Netflix's licences to stream shows are presumably for a limited period of time.
- There's only so much money in the pot, and they want to spend it on the renewals and new acquisitions they think mostly likely to retain existing subscribers and attract new ones.
- Sometimes the rights owner may not want to renew Netflix's licence: they may strike an exclusive deal with another platform, or may feel that having the show on Netflix is cannibalising their DVD sales.
I think Netflix though is making more off of their in-house produced content these days so I wouldn't say they're in danger, they just might look quite different in a few years.
Netflix is waay overhyped for what they actually have in their catalog.
https://www.amazon.com/Instant-Video/b?benefitId=xivetv&node...
You are not wrong
"US Netflix offers only 31 of IMDb's top 250 movies, study shows"
"David Wells, Netflix’s chief financial officer, was quoted by Variety stating that the company wants half of its content to be original productions over the next few years." [1]
RIP Netflix!
1. https://www.theguardian.com/film/2016/oct/14/us-netflix-imdb...
I find myself watching more of the stuff they've produced then the licensed content, so for me, it's been an improvement. If I run out of things to watch, maybe I'll care then, but at least I'm not tied to a cable contract, I can just switch to another streaming provider until they fix that issue. At this point though, I can't keep pace.
Why do so many not seem to understand that, in most cases, Netflix is likely not "dropping" anything? Netflix does not own the rights to the content and the owners can decide on the terms of the agreement on how long the content will be on Netflix.
People threatening to leave, or actually leaving, lowers Netflix's leverage to try to get and keep the content in their system.
This is why Netflix is creating their own content and bringing in content that's not normally within your area. So they can keep it up longer and have more control over their own destiny.
I started watching X-Files for the first time (yeah, never go around to it for the initial airing). Started watching in late February. And starting March 1st, is a warning on the upper left of my monitor saying "Show will be removed April 1".
From my viewpoint, I'm paying the same, and getting less. Not only less, but specifically something I'm trying to watch.
And yes, I tried watching that psychic alien abducted Netflix show. The sex scene at ep1 annoyed me, and had no interest still in ep3. Boring is putting it mildly.
Don't worry, the show goes downhill after the first couple of seasons, so it's probably for the best.
It's just as possible for the content owner to tell Amazon to stop selling their content if they wanted for some strange reason.
I have similar reasons to support HBO. However, I will not send money towards FOX/Comcast/Disney/Viacom because they still want to restrict their content and make it as hard as possible to watch it.
Of course, the danger is they could just start introducing advertising again by way of product placement in the story itself, but if it goes too far, I guess I'll cancel then.
It's a business model problem, and the entertainment model media providers offer is just not comparable to what is available on the internet.
Currently, I don't think the interest of Comcast's shareholders is the same as mine, they want to protect and grow their existing revenue streams, which depend on ads and outdated licensing contracts. My interest is to watch what I want, when I want, with as little friction as possible. Netflix and HBO provide that, none of the other big media companies do.
Also, Comcast has an interest in opposing net neutrality and providing a shitty internet experience so as to support their dying ad and TV business, so I also don't wish to support them for those reasons.
What a disappointing simplification.
And anything with 5 stars has a 50/50 chance of being worth watching.
So, for me, any simplification is welcome.
I basically rate like this: unbearable-cannot-finish (1), bad (2), okay-for-background-noise (3), what-I-expect (4), awesome (5).
Of course, some movies are somewhat between 3 and 4 or somewhat between 4 or 5. But if you just give me thumbs up or down there are like 80% of movies I cannot rate at all, because they are neither.
It just seems to me it's a simpler way to rate something by a user, and at the same time classify it more adequately.
Amazingly enough non-premium TV channels have better movies than Netflix, so we record them and skip the commercials.
Frankly, the only reason we keep Netflix is because there are a couple of kids TV series our kids like. We are using them to improve their Spanish language skills. If it weren't for that we would have cancelled a long time ago.
There are articles on Internet like "top 100 Netflix movies of the month" but it's almost always for the US Netflix.
So their recommendations may actually improve.
I know ratings/prediction has long been studied by the MovieLens.org scientists.
I'm finding interesting the rating system I've created and am trying to apply with some consistency to my Pocket archive of a few thousand items. Nominally it runs from 0 to 5, though I may reserve a 6 for an absolutely mind-blowing piece.
A 0 is a net negative: you are less informed for having read it, it reduces teh intelligence of its reader.
A 1 is, generally, a simple noting of some event.
A 2 should be a general news story, without strong insight.
A 3 is a news or general interest story with strong insight, or a typical scientific paper, or an undistinguished book (generally nonfiction).
A 4 is a particularly good scientific paper, or a typically well-thought-out book.
A 5 is a document which establishes a fundamental idea or field. Claude Shannon's original paper on information theory, say.
I don't think I've run across a 6 yet, but that might be a work which ties together two or more previously unrelated fields into a common theory.
My problem has been in assigning far too many '3' class articles. I've already carved out a list to re-assess and downgrade if appropriate.
I'd also like to be able to report on the numbers for each classification, though Pocket's utter lack of quantitative reporting (I cannot even state how many articles I've collected in total) stymies this.
I am ... increasingly dissastisfied with Pocket as an information management tool.
This is just my opinion now, but having studied recommender systems in a decent amount of depth, I don't think the design of the algorithm will need to change. The current techniques that provide the best results use matrix factorisation to simultaneously learn characteristics of films and how much each user likes each characteristic. My intuition would be that the algorithm can learn a lot more about a user from 3 up/down-ratings than from 1 star-rating, and the only reason Netflix are doing this is so that they can provide better recommendations so it's almost certainly the case.
TL;DR: All else being equal, 5-star ratings carry more signal for any machine learning algorithm, but the fact that thumbs up/down will result in 200% more data is a lot more significant than the delta in the signal.
I think the fundamental problem with different rating systems is that they are all one-dimensional. Maybe a two-category system, like 1-5 stars for "enjoyment" and another 1-5 stars for "quality". Or a 1-5 rating paired with a label, like "campy" or "serious", to prevent apples-and-oranges comparisons.
It's like how in Uber/Lyft, most people just default to 1/5 star ratings. In that case, the average rating a driver has to maintain gets skewed to a a pretty high number, like 4.5, and people who think "oh, this was a pretty good ride! 4 stars!" end up unintentionally boning the driver.
What I would like to see is a collection of all available movies by genre, and I'll pick what I want to watch. That's it - I don't need or want recommendations. In fact, if it's recommended, my first thought is "who's getting paid for that?"
My biased view is that if you are sick of bland streaming selections then you should really support MUBI. The more subscribers we can get the more we are able to fund great unique content. Our approach is fundamentally different from the data-driven, mass-appeal approach that tends to sand all the corners off of Netflix/Amazon content. Currently most MUBI content is licensed with a few exclusive releases sprinkled in, and the US selection is not as good as the UK, but as the subscriber base grows it will get better and better. Netflix et al are a volume game and the unit economics don't work out for them to purchase great indie films. If you care about great film as opposed to episodic content, MUBI is the streaming service that is pushing the envelope there.
Personally I use star ratings badly myself. I only ever rate titles 5 stars. But that's because I mostly only watch stuff I am fairly sure I am going to like.
That said, a simple thumbs up/down doesn't allow me to tell Netflix which movies were not bad and which to never show me again.
To me, 3-stars means: this movie has all the characteristics of something you'd enjoy - but we don't think you will.
Unfortunately, it's been more and more challenging for me to find 4- & 5- star stuff in their streaming interface.
Perhaps the thumbs-up will help expose those things, or at least help me hide the thumbs-down things.
I wish they would invest in a quality star rating system like Amazon's, rather than dumbing down the UI.
All in all this is a RIDICULOUS move. I wish I could download all my previous ratings before they remove them!
Now that mediocre content will be scattered across the two categories I really care about; I guess IMDB/Rotten tomatoes will steer clear of this ridiculous binary rating systems.
They kind of already know this though. (How many times have I watched a particular movie on Netflix?)
In any case, simplifying the ratings users can bestow makes a lot of sense, unfortunately. We like to imagine people carefully considering the merits and flaws like a professional reviewer, but the fact is that the vast majority of users only ever use two ratings anyway: if they like it, 5 stars; if they didn't, 1 star. There's maybe a third option where if they're ambivalent they just don't rate it.
So star ratings aren't actually very useful for evaluating products. This xkcd https://xkcd.com/1098/ made me realize that an Amazon product which has a 20% chance of exploding when you open the box, and otherwise works normally but unexceptionally, is gonna average out to a four-star rating for a product you really shouldn't buy. I always look at the one-star reviews before buying stuff now, no matter how few of them there are; knowing a thing's failure modes is much more useful than a bunch of praise.
You've seen this standup? How about watching it again a day later.
I'm all for this!
I feel as though this devalues their recommendations to you.
this seem like pretty retarded decision
at least we have TMDb where we can rate and discuss movies, unlike IMDb or Netflix
Just some of your recent posts on HN:
> why would i participate on dumb backup of database,
> done without my consent (OP stole 14 years of my posts),
> when I can go to proper movie database with discussions,
> imported ratings and watchlist from IMDb, called TMDb (themoviedb.org)
Love the fake outrage about having your comments "stolen", as if anybody really cares.
> he doesn't and he illegally copied 14 years of my posts which
> i don't like especially since I moved to TMDb which is proper
> movie database where you can also import your IMDb ratings
> and watchlist and each movie there has own discussions
> i would recommend checking TMDb which launched discussions
> for each listed title just yesterday, so don't expect there
> will be much content, but at least each movie or TV show had
> its own page and own forum, much more neat than reddit
ctrl-f for "TMDb" on each page of your comment history and you'll see the problem.
not sure where you got some fake outrage from my post, when some thief is stealing 14 years worth of my content from IMDb
judging by your language you are most likely shill from some competitors failed forums which are already forgotten as IMDb alternatives
everyone feel free to go through my history to see if I am some account made to spam with TMDb as this expert imply
36 mentions in 76 days.
https://hn.algolia.com/?query=author:Markoff%20tmdb&sort=byP...
Please keep this sort of tit-for-tat off HN.
I don't think you're a spammer and it's ok to occasionally promote your own stuff on HN in relevant contexts, but if you do it too much then other readers here will start to strenuously object.
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