‘Liking’ your content costs me nothing, so what’s to stop me from ‘Liking’ everything I see without considering how it affects the popularity of that content?
Is there evidence that a significant # of users are doing this?
Even if it is a problem, it's a solvable one. One thing that I did, years ago, when trying to create an algorithm for what stuff you would find funny, was to entirely discard the input from the users with the top n% of favorites/likes.
Basically, we determined that some people were just less discerning than others.
There's probably better ways of doing this. Some people probably "Like" a bunch of things because they are less discerning, others do it because they are legitimate social leaders in their circles. So you may be able to do more if you have some way of knowing how many things someone sees and chooses not to like, or how many friends subsequently view based on a "Like" (which I suspect Facebook already considers when choosing what to show on timelines).
I agree they are a flawed currency, I don't agree on your solution.
Not only is 10 an arbitrary number, but it sounds like a lot less than some people would like to give, and a lot more than most people will give any way.
Why not have the best of both worlds by using a PageRank-alike system to determine the weight of any given like.
That way, if someone is spamming likes they won't affect any one item very much compared to someone who likes only sparingly. You can go the whole hog and have the system where heavily liked users are more powerful than non-heavily liked users also.
So how would you weight them? If you assume the 10 likes/day "baseline", then would that indicate that if you moved above this average, your likes would be weighted down?
Let's face it, a "like" is not a "currency" - it's a reward system that simply exists to virally promote the rubric from which it spawns - ie, likes are a metric for social network to show their investors user engagement and activity.
That users somehow derive value or worth from getting likes is purely incidental. I mean, what's a "like" without a good link, message or photo to accompany it? Vague and useless.
I have considered more complex systems (i.e. dynamically altering the value of recommendations) and it's something that's likely to feature in the future. However I needed a simple solution for now whilst the site is developing, and I took a rough stab at it!
I'm very likely to adjust the limit (if not alter the system in the future) based on how it's used, but I'm waiting for more data before I make that jump :)
"Because you can only click ‘Recommend’ 10 times in a day, you can’t go gallavanting about throwing them hither and thither; they are to be reserved for the photos which you think other users should see."
This just puts the problem somewhere else.
Now, the new problem is a follower becoming "a flawed" currency. People who try to bot likes will now bot followers to get more likes.
I disagree. Considering the way likes work on platforms like Facebook, by Liking something you are advertising your interest in it. Viewers of your profile can see (and judge) the things that you have liked and also you create a story visibls to you friends in the ticker when you Like something. I know some of this is inherent to Facebook, but similar features likely exist on other platforms as well.
Capping it artificially stifles users who may actually enjoy photography more than others. A critic would presumable like photos far more often than a casual user.
I agree, saying likes are a(or resemble) currency is dubious preposition at best. FWIW likes as an idea is distorted, there are no unlikes so meaning you have no likes proposes that you have dislikes - except that you don't have way to measure disapproval. Consequently if you would be deprived of likes it would not devalue your work in most but most of the extreme cases.
Likes don't deliver any serious value to all but the most popular of internet celebrities. They do feel good though but you can't pay rent with likes.
There's an interesting idea here, but it takes some teasing out.
For one thing we need to pin down the word "like", which means something specific on Facebook and often something different in other places unless those places are playing the Facebook like game.
My gut feeling about social is that it a game of statistical sampling so it never hurts to get more data, you can always decide to throw some of it out later. So getting as many interactions as possible makes sense.
Sorry for any oddities in the way the article is written; I haven't posted anything on a blog in quite a while and I'm a bit rusty :)
More data is definitely needed and I'm open to altering the system once I understand better how it's working; this is very much an initial stab at improving the situation!
Likes on facebook are a seriously heinous currency.
"Please like us on facebook to unlock this functionality of software that you thought you already had full access to."
"To enter this contest, like us on facebook."
Now, the number of like's your product has is completely uncorrelated to the number of people who actually "Like" your product. You are effectively saying "look at all of these people we convinced to arbitrarily click a button"
They're called "Likes" because they represent what people like. You are setting a limit on the number of things people like? Your problem isn't with the concept of likes, it's that you wish they solved a problem which they don't solve.
You wish they were "recommendations" instead. But this is already a solved problem. When I like something enough that I want to recommend it to someone else, I use email. Or I call. Or I mention it in the hallway. Or I comment about it on Google+.
This type of recommendation existed long before we were born. It doesn't really need any help from you, but if you want to add a "recommendation" feature, go for it. But this concept doesn't replace the completely different concept you might call "like-based recommendations". This is a new concept that wasn't possible before, and now you're talking about crippling it by setting artificial limits!
The claim is that likes are a flawed currency to represent what people like because the cost of clicking a button on a web site trades for an infinitesimal amount of liking, so your representation winds being nearly useless to predict reality. Placing a limit on the "like" action to force people to choose is one potential way to increase the real value of the representation, hopefully extracting a stronger, more usable signal.
Right, but this solution tries to fit what is otherwise continuous into discrete so his calculations are easier. It is naive and unrealistic. I've never seen that succeed, and the 'approximations' people do with those kind of numbers are scary.
Are all likes equal in weight? Does my affinity remain constant over time? Am I capable of liking a countable number of concepts? Do I naturally revisit all items I've liked in my past life and re-evaluate if they make my cut?
The solution isn't in limiting likes, it's from finding a way to measure 'likes' using actual human behavior passively. For example, on newegg, sorting by 'most popular harddrive' is usually a better proxy than 'highest rated' for the quality I'm looking for. People vote with their wallets, and newegg was among the first to offer that data to inform recommendations. The tradeoff is this behavior helps entrench the winners where markets follow power laws, and that is why Amazon, for example, doesn't offer a similar feature (Amazon's is 'popular and new').
There will be a startup (if there isn't already), who will be acquired by a bank, designed to mine mint data with a POS receipt log and turn that into marketing data (customers who buy these bananas also buy gym membership at ...). JPMorgan (I think?) bought a groupon competitor for this reason. Facebook is tackling this from the other direction by combining browsing history with experian.
Don't people "voting with their wallets" send a very strong signal precisely because people's wallets are limited? That seems to argue in the direction of the article to me. It also doesn't seem intuitively obvious to me that there shouldn't be more active signals than that with nonzero value.
Indeed, but cash is (almost) continuous rather than discrete. Some kind of scarce recommendation system is probably a good idea, but 10/day feels like a very arbitrary cutoff.
So don't call it a "like". Call it something else, say "props" or "kudos".
The whole point here is to establish a currency for acknowledging quality, it need not represent actual "liking" of anything.
There is a deeper point here which is: what is a like anyways? Clicking a button to indicate you enjoy something has no use for the user. On Facebook users do it as a form of social signaling. On some other sites "liking" is essentially bookmarking for later use.
It's important to separate the actual utility of clicking a button vs. the concept notion of liking something.
> "You wish they were "recommendations" instead. But this is already a solved problem."
You're conflating two things. What you're talking about is sharing - you share a restaurant with a friend, or you share a funny cat picture with your cousin. "Recommendation" in this context means generating, programmatically, relevant things for you to look at. The power of "likes" (or any kind of quality score, really) is that you don't need to know the user's social graph to locate for them content they will be interested in. Even in a social networking context, a quality score allows you to expose the user to good content outside of their immediate social sphere, which on a photography website is certainly a desired feature.
The website in question is not about viewing the photo feeds of your internet friends, but about discovering the best content across the entire community, even from users who are completely disconnected from you in the social graph.
Unless I'm reading this blog post horribly wrong, when you click on "Recommend" you don't direct this recommendation towards anyone at all. It's simply "I mark this with my stamp of approval, which in turn will help surface this good content to other users of this website". There is not a social angle to this at all, nor any sort of directed sharing.
Anyways, I think this is a great idea. There was a website I used a long time ago called thesixtyone. It was an indie music site where discoverability was the core feature. You got "hearts" which you can give to specific songs. Your hearts replenish daily (use them or lose them), making them a currency, and preventing you from just heart'ing everything willy-nilly.
There's an elegance to this system that photographer.io can borrow: if you heart something (and therefore expose it to more users) and they in turn also heart the song, you get more hearts to spend. Essentially, users with taste that the community agrees with will get a louder voice, and the community becomes more directed as a result. It also allows you very clear visibility into influencers in the community.
There was also a neat gamification angle to it that gave achievements for tasks, with the overall goal of making sure you spend your hearts instead of doing nothing with them.
> Unless I'm reading this blog post horribly wrong, when you click on "Recommend" you don't direct this recommendation towards anyone at all. It's simply "I mark this with my stamp of approval, which in turn will help surface this good content to other users of this website". There is not a social angle to this at all, nor any sort of directed sharing.
You're not wrong - that's exactly how it works! I must apologise for the post being quite unclear; it has been quite a while since I wrote anything :)
Discoverability is very much one of the core concerns for me, as I feel it's lacking elsewhere. I like the idea of rewarding people that others agree with, as that could help encourage users to recommend more often whilst also making exploitation of the system more tricky.
This is still very much in its early stages and I'm keen to improve on it. Thanks for the great feedback!
Why not just weighting the value of each like by the number of likes given over a period of time? People who spare the likes will give more value, those who like it all, won´t really add much value with their like.
There is certainly negative pressure on likes. For well meaning users, likes reflect what you like, which means they reflect you. In the case of Facebook, the platform also advertises what you like to others.
People are strongly motivated to look good in the eyes of others, so most people don't like everything, that's just creepy.
Are you sure FB doesn't rate limit the exposure of your likes to uninvolved parties? I am too stingy with likes, but have fantasized about going bananas.
With Facebook, nobody gets your likes (well except for your friends, or any Pages you like).
You can, however, as an advertiser target people who "like" particular things but you don't find out who they are (unless they click off site and sign up for your service).
The way TFA puts the matter, this looks like an Information Retrieval problem. That is, the author is looking for a data stream that can be used to inform search relevance. One might imagine collecting other active or passive metrics (e.g. "click through from thumbnail", "time on page", "times shared") to inform relevance as well.
Another question I'd pose: what's the real goal of a "like" in the various contexts it is used? In many Facebook applications (e.g. liking a photo, comment, etc.), it appears to be as much about filling in missing social cueing -- a one-bit emoticon, if you will -- than about informing relevance. In other contexts on Facebook, there's a definite notion of relevance (e.g. liking a page).
Given that, I suppose I must ask: how much additional value can really be added to this single low-information stream vs. simply using additional active or passive metrics to inform relevance? Is "like dilution" really such a big problem? If it is, there are other approaches to consider.
Instead of creating artificial scarcity it may be worth using another IR technique and inverse-weight a signal based on dilution criteria. I.e. if one user expends LOTS of "likes", they effectively place a lower value on each of those. If another user uses "likes" very infrequently, we can infer those carry more weight. A straw-man example: turning the proposed technique on its head, the summed value of a user's likes might be capped at 10 points each [week, etc.], with no limit on the number of likes. The value of any like within that window is 10/[number of likes this week]. Users can click the "recommend/like/etc" button as many times as they want, and the system will dutifully record and interpret that data according to the site's needs.
Your last paragraph is something I've been considering as a next evolution of how it's implemented, but I'm waiting for more data (and time!) before I take a look at developing it further :)
Likes are not meant to be a currency at all, it's meant to be an engagement tool with minimal friction. It's not about ranking anything, it's about making it so easy to say "check out this cool thing I found" that anyone can do it.
Anecdotally, I used to surf the internet a lot in high school in the late 90's/early 00's and not a ton of my friends were online, so there wasn't much shared link wise among us. Then, in college, there was a group forum that a bunch of us were a part of and one of the more popular parts of it were the links people posted. Then, after college digg, Facebook, reddit, pintrest and twitter all found a place amongst the population at large for sharing things.
I guess for most people sharing is not about currency or ranking at all, it's about sharing something interesting. It's more akin to telling a friend about music that you like, a book that you are reading, or a newspaper article you thought was insightful. When you think of those things, it is rarely about ranking them, it is almost always about sharing.
People share with other people because it makes their life better or their friends' lives better. They do it because they care about their friends, not because they care about you or what you created.
Bad headline given the use case which not a currency issue but rather how to feature quality content that is not as popular:
* Discovery: Good but unpopular content is being buried by popular content
* The noise makers are the taste makers: What is popular is being decided by the most active users
* Popular begets more popular: Rankings that are based on popularity only reinforces the popularity of top-ranked items.
Some communities may decide that this is not a problem and therefore ranking content by Likes works as is.
However if you want to address those issues, then yes, rationing the available Likes is one way to approach it. You could also weight the ranking formulas by percentage of Likes per user (ie. users that have older accounts and give out less Likes overall are weighted differently)
It seems to me that the problem here is not that likes mean the wrong thing, it is that the developer wants to post-process likes to create a metric that means something and it isn't working out to what it desired.
The simple solution is to post-process likes in a different way. See http://www.evanmiller.org/how-not-to-sort-by-average-rating.... for a suggestion that makes them more like a rating, despite the fact that some people use them differently than others.
But we can do other things. Here is a fun one. If a person likes 2 different photos, we can say that is a connection between them of strength 1/log(# photos liked by that person). The total connection between any two photos is the sum of the connections. From each photo we can now find other photos that are in some sense likely to be liked by people who like the first. People enjoy following connections like these.
So the challenge is not to remove likes as a mechanism because it doesn't give you the information that you want. It is to find ways to process the data you get from it to pull out useful information.
Thanks for the links; I'll definitely be reading through those later (as this field is very interesting to me)!
I think I may have confused matters somewhat by comparing directly to a 'Like', which is more like a 0-1 rating system, rather than to a combination of Liking and Sharing. How far it goes towards each of those is something I'm experimenting with :)
> Here is a fun one. If a person likes 2 different photos, we can say that is a connection between them of strength 1/log(# photos liked by that person). The total connection between any two photos is the sum of the connections. From each photo we can now find other photos that are in some sense likely to be liked by people who like the first. People enjoy following connections like these.
I can't wait until I have more data to play with and I can start experimenting with ways of connecting photos like you say; I've not often had the chance to try out some of these ideas due to the limited size of datasets I've had in the past.
It alerts everyone that I'm friends with that I "liked" something.
I'm not going to go around handing out likes for things because it will make me seem annoying and not genuine. I also don't want to represent myself as someone that likes Wal-Mart or Samsung unless those are things I actually like.
It also costs you information about yourself that you give up to say, facebook. While its value is probably tiny and arguably hard to convert into real currency it still cost you something. Very little, but something.
I handle this slightly differently in that your recommendations are not publicly browsable, and should they become so then that will be disableable by a configuration option. I very much want to use it as a method of exploration, rather than advertising.
Beyond the various aspects of social utility of "Like" that everyone has mentioned, a 'Like' can also mean that you want to subscribe to updates from a source, such as you would do with RSS.
Aye, it does seem to be a very interchangeable term. I use 'Like' in the article much as you would see on Facebook. I currently have a simplistic following feature implemented but it's on the roadmap to offer suggestions based on what you've favourited or recommended in the past too.
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[ 4.0 ms ] story [ 111 ms ] threadIs there evidence that a significant # of users are doing this?
Basically, we determined that some people were just less discerning than others.
Not only is 10 an arbitrary number, but it sounds like a lot less than some people would like to give, and a lot more than most people will give any way.
Why not have the best of both worlds by using a PageRank-alike system to determine the weight of any given like.
That way, if someone is spamming likes they won't affect any one item very much compared to someone who likes only sparingly. You can go the whole hog and have the system where heavily liked users are more powerful than non-heavily liked users also.
Let's face it, a "like" is not a "currency" - it's a reward system that simply exists to virally promote the rubric from which it spawns - ie, likes are a metric for social network to show their investors user engagement and activity.
That users somehow derive value or worth from getting likes is purely incidental. I mean, what's a "like" without a good link, message or photo to accompany it? Vague and useless.
I've probably Liked 15-20 things, total, since the Like feature was introduced.
I'm very likely to adjust the limit (if not alter the system in the future) based on how it's used, but I'm waiting for more data before I make that jump :)
This just puts the problem somewhere else.
Now, the new problem is a follower becoming "a flawed" currency. People who try to bot likes will now bot followers to get more likes.
Capping it artificially stifles users who may actually enjoy photography more than others. A critic would presumable like photos far more often than a casual user.
Likes don't deliver any serious value to all but the most popular of internet celebrities. They do feel good though but you can't pay rent with likes.
IMO YMMW.
For one thing we need to pin down the word "like", which means something specific on Facebook and often something different in other places unless those places are playing the Facebook like game.
My gut feeling about social is that it a game of statistical sampling so it never hurts to get more data, you can always decide to throw some of it out later. So getting as many interactions as possible makes sense.
More data is definitely needed and I'm open to altering the system once I understand better how it's working; this is very much an initial stab at improving the situation!
"Please like us on facebook to unlock this functionality of software that you thought you already had full access to."
"To enter this contest, like us on facebook."
Now, the number of like's your product has is completely uncorrelated to the number of people who actually "Like" your product. You are effectively saying "look at all of these people we convinced to arbitrarily click a button"
You wish they were "recommendations" instead. But this is already a solved problem. When I like something enough that I want to recommend it to someone else, I use email. Or I call. Or I mention it in the hallway. Or I comment about it on Google+.
This type of recommendation existed long before we were born. It doesn't really need any help from you, but if you want to add a "recommendation" feature, go for it. But this concept doesn't replace the completely different concept you might call "like-based recommendations". This is a new concept that wasn't possible before, and now you're talking about crippling it by setting artificial limits!
Are all likes equal in weight? Does my affinity remain constant over time? Am I capable of liking a countable number of concepts? Do I naturally revisit all items I've liked in my past life and re-evaluate if they make my cut?
The solution isn't in limiting likes, it's from finding a way to measure 'likes' using actual human behavior passively. For example, on newegg, sorting by 'most popular harddrive' is usually a better proxy than 'highest rated' for the quality I'm looking for. People vote with their wallets, and newegg was among the first to offer that data to inform recommendations. The tradeoff is this behavior helps entrench the winners where markets follow power laws, and that is why Amazon, for example, doesn't offer a similar feature (Amazon's is 'popular and new').
There will be a startup (if there isn't already), who will be acquired by a bank, designed to mine mint data with a POS receipt log and turn that into marketing data (customers who buy these bananas also buy gym membership at ...). JPMorgan (I think?) bought a groupon competitor for this reason. Facebook is tackling this from the other direction by combining browsing history with experian.
The whole point here is to establish a currency for acknowledging quality, it need not represent actual "liking" of anything.
There is a deeper point here which is: what is a like anyways? Clicking a button to indicate you enjoy something has no use for the user. On Facebook users do it as a form of social signaling. On some other sites "liking" is essentially bookmarking for later use.
It's important to separate the actual utility of clicking a button vs. the concept notion of liking something.
> "You wish they were "recommendations" instead. But this is already a solved problem."
You're conflating two things. What you're talking about is sharing - you share a restaurant with a friend, or you share a funny cat picture with your cousin. "Recommendation" in this context means generating, programmatically, relevant things for you to look at. The power of "likes" (or any kind of quality score, really) is that you don't need to know the user's social graph to locate for them content they will be interested in. Even in a social networking context, a quality score allows you to expose the user to good content outside of their immediate social sphere, which on a photography website is certainly a desired feature.
The website in question is not about viewing the photo feeds of your internet friends, but about discovering the best content across the entire community, even from users who are completely disconnected from you in the social graph.
Unless I'm reading this blog post horribly wrong, when you click on "Recommend" you don't direct this recommendation towards anyone at all. It's simply "I mark this with my stamp of approval, which in turn will help surface this good content to other users of this website". There is not a social angle to this at all, nor any sort of directed sharing.
Anyways, I think this is a great idea. There was a website I used a long time ago called thesixtyone. It was an indie music site where discoverability was the core feature. You got "hearts" which you can give to specific songs. Your hearts replenish daily (use them or lose them), making them a currency, and preventing you from just heart'ing everything willy-nilly.
There's an elegance to this system that photographer.io can borrow: if you heart something (and therefore expose it to more users) and they in turn also heart the song, you get more hearts to spend. Essentially, users with taste that the community agrees with will get a louder voice, and the community becomes more directed as a result. It also allows you very clear visibility into influencers in the community.
There was also a neat gamification angle to it that gave achievements for tasks, with the overall goal of making sure you spend your hearts instead of doing nothing with them.
You're not wrong - that's exactly how it works! I must apologise for the post being quite unclear; it has been quite a while since I wrote anything :)
Discoverability is very much one of the core concerns for me, as I feel it's lacking elsewhere. I like the idea of rewarding people that others agree with, as that could help encourage users to recommend more often whilst also making exploitation of the system more tricky.
This is still very much in its early stages and I'm keen to improve on it. Thanks for the great feedback!
People are strongly motivated to look good in the eyes of others, so most people don't like everything, that's just creepy.
You can, however, as an advertiser target people who "like" particular things but you don't find out who they are (unless they click off site and sign up for your service).
Another question I'd pose: what's the real goal of a "like" in the various contexts it is used? In many Facebook applications (e.g. liking a photo, comment, etc.), it appears to be as much about filling in missing social cueing -- a one-bit emoticon, if you will -- than about informing relevance. In other contexts on Facebook, there's a definite notion of relevance (e.g. liking a page).
Given that, I suppose I must ask: how much additional value can really be added to this single low-information stream vs. simply using additional active or passive metrics to inform relevance? Is "like dilution" really such a big problem? If it is, there are other approaches to consider.
Instead of creating artificial scarcity it may be worth using another IR technique and inverse-weight a signal based on dilution criteria. I.e. if one user expends LOTS of "likes", they effectively place a lower value on each of those. If another user uses "likes" very infrequently, we can infer those carry more weight. A straw-man example: turning the proposed technique on its head, the summed value of a user's likes might be capped at 10 points each [week, etc.], with no limit on the number of likes. The value of any like within that window is 10/[number of likes this week]. Users can click the "recommend/like/etc" button as many times as they want, and the system will dutifully record and interpret that data according to the site's needs.
Your last paragraph is something I've been considering as a next evolution of how it's implemented, but I'm waiting for more data (and time!) before I take a look at developing it further :)
Anecdotally, I used to surf the internet a lot in high school in the late 90's/early 00's and not a ton of my friends were online, so there wasn't much shared link wise among us. Then, in college, there was a group forum that a bunch of us were a part of and one of the more popular parts of it were the links people posted. Then, after college digg, Facebook, reddit, pintrest and twitter all found a place amongst the population at large for sharing things.
I guess for most people sharing is not about currency or ranking at all, it's about sharing something interesting. It's more akin to telling a friend about music that you like, a book that you are reading, or a newspaper article you thought was insightful. When you think of those things, it is rarely about ranking them, it is almost always about sharing.
People share with other people because it makes their life better or their friends' lives better. They do it because they care about their friends, not because they care about you or what you created.
Likes that cause donations to go to your favorite charity!
(I am an acquaintance of one of the founders, but not related in any other way.)
* Discovery: Good but unpopular content is being buried by popular content
* The noise makers are the taste makers: What is popular is being decided by the most active users
* Popular begets more popular: Rankings that are based on popularity only reinforces the popularity of top-ranked items.
Some communities may decide that this is not a problem and therefore ranking content by Likes works as is.
However if you want to address those issues, then yes, rationing the available Likes is one way to approach it. You could also weight the ranking formulas by percentage of Likes per user (ie. users that have older accounts and give out less Likes overall are weighted differently)
The simple solution is to post-process likes in a different way. See http://www.evanmiller.org/how-not-to-sort-by-average-rating.... for a suggestion that makes them more like a rating, despite the fact that some people use them differently than others.
But we can do other things. Here is a fun one. If a person likes 2 different photos, we can say that is a connection between them of strength 1/log(# photos liked by that person). The total connection between any two photos is the sum of the connections. From each photo we can now find other photos that are in some sense likely to be liked by people who like the first. People enjoy following connections like these.
This system is similar in principle to the one that I wrote about at http://bentilly.blogspot.com/2011/02/finding-related-items.h.... Which drove a noticeable sales bump.
So the challenge is not to remove likes as a mechanism because it doesn't give you the information that you want. It is to find ways to process the data you get from it to pull out useful information.
I think I may have confused matters somewhat by comparing directly to a 'Like', which is more like a 0-1 rating system, rather than to a combination of Liking and Sharing. How far it goes towards each of those is something I'm experimenting with :)
> Here is a fun one. If a person likes 2 different photos, we can say that is a connection between them of strength 1/log(# photos liked by that person). The total connection between any two photos is the sum of the connections. From each photo we can now find other photos that are in some sense likely to be liked by people who like the first. People enjoy following connections like these.
I can't wait until I have more data to play with and I can start experimenting with ways of connecting photos like you say; I've not often had the chance to try out some of these ideas due to the limited size of datasets I've had in the past.
It alerts everyone that I'm friends with that I "liked" something.
I'm not going to go around handing out likes for things because it will make me seem annoying and not genuine. I also don't want to represent myself as someone that likes Wal-Mart or Samsung unless those are things I actually like.