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I’m not thrilled about the amount of information being hoovered up, but....the predictions being made with it aren’t terribly impressive. Facebook should know

I’m not thrilled about the amount of information being hoovered up, but....the predictions being made with it aren’t terribly impressive. Facebook should know a great deal about me, but the advertising categories it puts me in are either completely obvious (based on locations and group memberships that I’ve explicitly told it), or bonkers. It currently thinks I’m part of multiple wildly incompatible religions and political groups and am interested in a weird collection of abstract concepts (“decay”?) Amazon thinks that my interests are dominated by textbooks and vacuum cleaners (if only!) Twitter has correctly sussed out that someone who mostly follows scientists might be interested in science....or dogs.

robertAngst 8y ago
Came here to say this. From my own experience + an Amazon Software engineer agreed. You can do lots with data, but in the end, many times its uselessly applied.
qaq 8y ago
Totally agree I wonder if they ever had a control group that they showed things in random just to measure how good their tech is.
- marcinzm 8y ago
  Possibly, I used to work for a smaller ad company and one of the tests we did was random ranking of ads. Problem is that it can be a very expensive test to run so you wouldn't run it on anything except a tiny fraction of traffic.
- nostrademons 8y ago
  Dunno about Facebook/Amazon/Twitter, but Google uses a control group on every single experiment. They also usually have holdbacks (control groups over time, where the a small user population does not receive a new feature to measure the performance as people get accustomed to it) and ablation experiments (where a previously-launched feature is turned off for a small population of users to verify the expected loss in performance). Advertising has always been a business of extremely large numbers. The chance that a given user will purchase a product because of an ad is imperceptible; aggregated over hundreds of millions of ads shown, imperceptible changes add up to large increases in sales. People tend to forget just how bad ads were before Google & Facebook; it's not that the latter have solved advertising, it's that TV, print, and billboards were so terrible that it didn't take much to improve markedly over them.
marcinzm 8y ago
Amazon has emailed me multiple times to answer questions about an item I never bought (in the email they literally say "as the owner of" which I'm not). If they can't even get that right then I suspect they have a lot of data quality issues which would result in bad predictions.
deleted 8y ago
[deleted]
luckylion 8y ago
> I’m not thrilled about the amount of information being hoovered up, but....the predictions being made with it aren’t terribly impressive. Conspiracy theory: what if they intentionally throw random garbage in there so you don't get paranoid? The things they want to hit you with will still be there, but they'll be surrounded by misses, and you're inclined to think "wow, they sent me an ad for a new dishwasher just as soon as mine broke, but they also sent me an ad for cat toys knowing full well I'm allergic to cats"? The dishwasher is still a great hit, but less suspiciously so.
- ardy42 8y ago
  > Conspiracy theory: what if they intentionally throw random garbage in there so you don't get paranoid? That's not a paranoid conspiracy theory (and certainly not something that should be down-voted to oblivion). It's well documented that Target did that exact same thing to throw off its customers, so they wouldn't be weirded-out by its pregnancy-prediction algorithm. https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/ https://www.forbes.com/sites/kashmirhill/2012/02/16/how-targ...: > “Then we started mixing in all these ads for things we knew pregnant women would never buy, so the baby ads looked random. We’d put an ad for a lawn mower next to diapers. We’d put a coupon for wineglasses next to infant clothes. That way, it looked like all the products were chosen by chance. > “And we found out that as long as a pregnant woman thinks she hasn’t been spied on, she’ll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don’t spook her, it works.”
  - mattkrause 8y ago
    Predicting a pregnancy sounds so creepy and personal, but it actually seems like it should be one of the easiest life events for a retailer to predict. The story plays up changes in consumption (scented->unscented lotion) but there are also whole categories of products that are used only by pregnant women (e.g., maternity clothes, pre/perinatal vitamins). Another huge swath of the store is devoted to infants (clothes, diapers, toys, formula). There are also really strong demographic priors (women only, 18-40 or so, though you could fine-tune that age bracket much more precisely with socioeconomic status (credit score?) or zip code.
cirgue 8y ago
I do modeling for an advertising recommendation engine (not at Amazon). We do controls against both human-made rulesets and random, and we consistently outperform both by a big margin. However, click through rate is always really low, and a model that massively increases revenue still serves irrelevant content most of the time, and still seems random from the POV of the user. The point of ML in advertising recommendation is to guess better in aggregate, not get it right all the time.
- b_tterc_p 8y ago
  What about human models plus baseline likeliness to click on any ad?
  - manicdee 8y ago
    When the machines can predict high click-through rates, humans will be obsolete. What is even scarier is when ads don’t generate click-through rates at all, rather success will be measured by “subject A saw this propaganda” correlated to “subject A subsequently performed this action.”
    - b_tterc_p 8y ago
      Well basically yes. Cambridge analytica more or less measured how stupid you were and rationed out appropriately outlandish lies. What I’m asking is whether we can establish your baseline likelihood of clicking any ad, and then use human made models to determine placements. My hypothesis is that naive human models only lose out because they miss the low hanging fruit of avoid people who never click anythingf
      - manicdee 8y ago
        What if the reason click through are so low is that there is little demand created by marketing and only a relatively low rate of unexpressed demand? In my case, when I want something I will go find it. I spent three months looking for reviews of small, efficient cars before deciding on a VW Polo TDI. The day I went in to buy one, the salesman was only interested in selling stock they had in the floor that day (it was some kind of stocktake clearance) and none of those were TDI. He was extremely rude too. So I walked out of that dealership and bought a Mazda 2 Then I got three months of ads on various sites advertising Mazda 2. Never any ads for small, fuel efficient cars while that was what I was searching for on Google. The magic of targeted advertising completely failed to work. For the next few years, any ads for small efficient cars will be absolutely useless because I have no need for another one. And for some reason I am getting lots of ads for women’s clothing. I suspect this is based on assumptions about men in long term relationships looking for a bit on the side, but once again those ads miss their mark because I have never bought womens clothing, I do not visit sugar daddy dating sites, so I have no idea why they think I should be interested. It’s not just “people who don’t click anything,” it’s “people who aren’t interested in what you are selling,” and “the thing you are selling is not an impulse buy.” Nobody buys a second economy car as an impulse buy. A good deal on a 600mm Nikon F Mount might catch my interest though (but they’re all the same price as my car, so I don’t necessarily have that kind of money)
pdkl95 8y ago
> the predictions being made with it aren’t terribly impressive So what? They still have the data and can refine their methods tomorrow. Today their predictions might be low quality, but they can retry as many times as they want. The problem is not the predictions they are making today; it's the many predictions (or inferences) are able to keep making in the future. > political groups Remember that some types of advertising is not targeted. Some political advertising or branding advertising is intended to reach "all voters"j or maybe a very broad category like "Everyone Californian of voting age". Branding campaigns don't care if you're interested in e.g. vacuum cleaners. They just want you to think of their name first every time you happen to think of or hear about vacuum cleaners. edit: (Multiple contradicting groups could be pushing ads at your (very general) demographic.) > science....or dogs Many scientists like dogs? > Amazon thinks that my interests No, they think that showing you textbooks and vacuum cleaners has a greater chance of increasing their revenue, according to various statistical models. Targeted advertising isn't about targeting what your are interested in. It's about letting other people target your with what they think they can sell you. edit2: Of course, it could also be a terrible model trying to use data in stupid ways. I'm just suggesting that there are many plausible explanations.
- mattkrause 8y ago
  Sure; It's obviously a mix of clever and stupid. One of the more memorable political groups was the Sandinistas. I have never been to Nicaragua, so....limited relevance. I actually just looked (increasing my likelihood to be targeted again?), and was surprised to learn that they're still around; I had no idea they out-lasted Iran-Contra. As for Amazon, I agree that P(bought a vacuum) is a good signal for P(will eventually buy another vacuum). Still, if they have this detailed psychographic model of me, you'd think they could do slightly better than what is essentially a zeroth-order prediction.
  - pdkl95 8y ago
    > I have never been to Nicaragua, so....limited relevance Ok, that's probably some sort of error. > you'd think they could do slightly better It depends on how they made their model. With machine learning, it's easy to build something that does improve the whatever high-level metric they were tying to optimize (like "revenue") , but gives essentially random results for many individuals. This is especially true for any anything that wasn't fully covered in the training data/; a handwritten digit recognizer may work wonderfully on average, while giving random results random output when presented with anything that wasn't covered by the MNIST dataabsse. Sometimes this can be especially fragile[1]! [1] https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196 https://medium.com/@ageitgey/machine-learning-is-fun-part-8-...
brudgers 8y ago
Low cost drives ordinary predictions. The average prediction is probably a few milliseconds of in memory operations at best. No network calls. Certainly no IO's to disk. Even with more work, you're targeted because you're among the best matches within your zipcode. The best results does not imply good results. Ordinarily ads reflect what an advertiser specified not what the platform thinks is most likely to be successfully sold to you.
mlthoughts2018 8y ago
There’s a distinction between what these companies can predict about you vs what they can roll up into products that other entities want to pay for. For example, a ton of advertisers will predetermine some targeting criteria based on simple demographics, brand loyalty or rewards program data, etc. and then commit to it for a whole ad campaign, even if it means leaving money on the table by not electing to dynamically shift into more precise targeting. For these clients, no amount of fancy predictive capability or algorithmic targeting will matter, they just don’t care. So Facebook or whomever just offers them big, sloppy and easy-to-conceptualize segments or buckets of users. They just want aggregate intelligence anyway, so nobody in the transaction cares much if it’s wrong about your age bracket or general TV interests. This doesn’t mean Facebook is unable to produce far more alarming forecasts of your behavior, or assign you to categories based on things like political activism, privacy conscientiousness, or detect personal life details like your location trail, purchases, etc. The really scary stuff just doesn’t end up being surfaced in connection to lowest common denominator adtech products.
deogeo 8y ago
What if the data is used to find whistleblowers? Or predict them? I'm sure there's plenty of corporations and governments that will be willing to pay for such services. https://www.abc.net.au/triplej/programs/hack/how-team-of-pre-teens-found-whisteblower-using-metadata/8113668 https://www.abc.net.au/triplej/programs/hack/how-team-of-pre...
darkpuma 8y ago
Something you should keep in mind is that companies like Amazon are all well aware of the anecdote about Target and teenage pregnancies. And the lesson they learned from that story is they should conceal from the user the full extent of what they know about that user, to avoid creeping people out. If you receive 10 product recommendations, perhaps only one of them is actually targeted with a very high degree of confidence and the other 9 are basically noise added to deliberately deceive you, to lead you to believe Amazon knows less about you than they really do.

robertAngst 8y ago

Came here to say this. From my own experience + an Amazon Software engineer agreed. You can do lots with data, but in the end, many times its uselessly applied.

qaq 8y ago

Totally agree I wonder if they ever had a control group that they showed things in random just to measure how good their tech is.

marcinzm 8y ago
Possibly, I used to work for a smaller ad company and one of the tests we did was random ranking of ads. Problem is that it can be a very expensive test to run so you wouldn't run it on anything except a tiny fraction of traffic.
nostrademons 8y ago
Dunno about Facebook/Amazon/Twitter, but Google uses a control group on every single experiment. They also usually have holdbacks (control groups over time, where the a small user population does not receive a new feature to measure the performance as people get accustomed to it) and ablation experiments (where a previously-launched feature is turned off for a small population of users to verify the expected loss in performance). Advertising has always been a business of extremely large numbers. The chance that a given user will purchase a product because of an ad is imperceptible; aggregated over hundreds of millions of ads shown, imperceptible changes add up to large increases in sales. People tend to forget just how bad ads were before Google & Facebook; it's not that the latter have solved advertising, it's that TV, print, and billboards were so terrible that it didn't take much to improve markedly over them.

marcinzm 8y ago

Amazon has emailed me multiple times to answer questions about an item I never bought (in the email they literally say "as the owner of" which I'm not). If they can't even get that right then I suspect they have a lot of data quality issues which would result in bad predictions.

deleted 8y ago

[deleted]

luckylion 8y ago

> I’m not thrilled about the amount of information being hoovered up, but....the predictions being made with it aren’t terribly impressive. Conspiracy theory: what if they intentionally throw random garbage in there so you don't get paranoid? The things they want to hit you with will still be there, but they'll be surrounded by misses, and you're inclined to think "wow, they sent me an ad for a new dishwasher just as soon as mine broke, but they also sent me an ad for cat toys knowing full well I'm allergic to cats"? The dishwasher is still a great hit, but less suspiciously so.

ardy42 8y ago
> Conspiracy theory: what if they intentionally throw random garbage in there so you don't get paranoid? That's not a paranoid conspiracy theory (and certainly not something that should be down-voted to oblivion). It's well documented that Target did that exact same thing to throw off its customers, so they wouldn't be weirded-out by its pregnancy-prediction algorithm. https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/ https://www.forbes.com/sites/kashmirhill/2012/02/16/how-targ...: > “Then we started mixing in all these ads for things we knew pregnant women would never buy, so the baby ads looked random. We’d put an ad for a lawn mower next to diapers. We’d put a coupon for wineglasses next to infant clothes. That way, it looked like all the products were chosen by chance. > “And we found out that as long as a pregnant woman thinks she hasn’t been spied on, she’ll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don’t spook her, it works.”
- mattkrause 8y ago
  Predicting a pregnancy sounds so creepy and personal, but it actually seems like it should be one of the easiest life events for a retailer to predict. The story plays up changes in consumption (scented->unscented lotion) but there are also whole categories of products that are used only by pregnant women (e.g., maternity clothes, pre/perinatal vitamins). Another huge swath of the store is devoted to infants (clothes, diapers, toys, formula). There are also really strong demographic priors (women only, 18-40 or so, though you could fine-tune that age bracket much more precisely with socioeconomic status (credit score?) or zip code.

cirgue 8y ago

I do modeling for an advertising recommendation engine (not at Amazon). We do controls against both human-made rulesets and random, and we consistently outperform both by a big margin. However, click through rate is always really low, and a model that massively increases revenue still serves irrelevant content most of the time, and still seems random from the POV of the user. The point of ML in advertising recommendation is to guess better in aggregate, not get it right all the time.

b_tterc_p 8y ago
What about human models plus baseline likeliness to click on any ad?
- manicdee 8y ago
  When the machines can predict high click-through rates, humans will be obsolete. What is even scarier is when ads don’t generate click-through rates at all, rather success will be measured by “subject A saw this propaganda” correlated to “subject A subsequently performed this action.”
  - b_tterc_p 8y ago
    Well basically yes. Cambridge analytica more or less measured how stupid you were and rationed out appropriately outlandish lies. What I’m asking is whether we can establish your baseline likelihood of clicking any ad, and then use human made models to determine placements. My hypothesis is that naive human models only lose out because they miss the low hanging fruit of avoid people who never click anythingf
    - manicdee 8y ago
      What if the reason click through are so low is that there is little demand created by marketing and only a relatively low rate of unexpressed demand? In my case, when I want something I will go find it. I spent three months looking for reviews of small, efficient cars before deciding on a VW Polo TDI. The day I went in to buy one, the salesman was only interested in selling stock they had in the floor that day (it was some kind of stocktake clearance) and none of those were TDI. He was extremely rude too. So I walked out of that dealership and bought a Mazda 2 Then I got three months of ads on various sites advertising Mazda 2. Never any ads for small, fuel efficient cars while that was what I was searching for on Google. The magic of targeted advertising completely failed to work. For the next few years, any ads for small efficient cars will be absolutely useless because I have no need for another one. And for some reason I am getting lots of ads for women’s clothing. I suspect this is based on assumptions about men in long term relationships looking for a bit on the side, but once again those ads miss their mark because I have never bought womens clothing, I do not visit sugar daddy dating sites, so I have no idea why they think I should be interested. It’s not just “people who don’t click anything,” it’s “people who aren’t interested in what you are selling,” and “the thing you are selling is not an impulse buy.” Nobody buys a second economy car as an impulse buy. A good deal on a 600mm Nikon F Mount might catch my interest though (but they’re all the same price as my car, so I don’t necessarily have that kind of money)

pdkl95 8y ago

> the predictions being made with it aren’t terribly impressive So what? They still have the data and can refine their methods tomorrow. Today their predictions might be low quality, but they can retry as many times as they want. The problem is not the predictions they are making today; it's the many predictions (or inferences) are able to keep making in the future. > political groups Remember that some types of advertising is not targeted. Some political advertising or branding advertising is intended to reach "all voters"j or maybe a very broad category like "Everyone Californian of voting age". Branding campaigns don't care if you're interested in e.g. vacuum cleaners. They just want you to think of their name first every time you happen to think of or hear about vacuum cleaners. edit: (Multiple contradicting groups could be pushing ads at your (very general) demographic.) > science....or dogs Many scientists like dogs? > Amazon thinks that my interests No, they think that showing you textbooks and vacuum cleaners has a greater chance of increasing their revenue, according to various statistical models. Targeted advertising isn't about targeting what your are interested in. It's about letting other people target your with what they think they can sell you. edit2: Of course, it could also be a terrible model trying to use data in stupid ways. I'm just suggesting that there are many plausible explanations.

mattkrause 8y ago
Sure; It's obviously a mix of clever and stupid. One of the more memorable political groups was the Sandinistas. I have never been to Nicaragua, so....limited relevance. I actually just looked (increasing my likelihood to be targeted again?), and was surprised to learn that they're still around; I had no idea they out-lasted Iran-Contra. As for Amazon, I agree that P(bought a vacuum) is a good signal for P(will eventually buy another vacuum). Still, if they have this detailed psychographic model of me, you'd think they could do slightly better than what is essentially a zeroth-order prediction.
- pdkl95 8y ago
  > I have never been to Nicaragua, so....limited relevance Ok, that's probably some sort of error. > you'd think they could do slightly better It depends on how they made their model. With machine learning, it's easy to build something that does improve the whatever high-level metric they were tying to optimize (like "revenue") , but gives essentially random results for many individuals. This is especially true for any anything that wasn't fully covered in the training data/; a handwritten digit recognizer may work wonderfully on average, while giving random results random output when presented with anything that wasn't covered by the MNIST dataabsse. Sometimes this can be especially fragile[1]! [1] https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196 https://medium.com/@ageitgey/machine-learning-is-fun-part-8-...

brudgers 8y ago

Low cost drives ordinary predictions. The average prediction is probably a few milliseconds of in memory operations at best. No network calls. Certainly no IO's to disk. Even with more work, you're targeted because you're among the best matches within your zipcode. The best results does not imply good results. Ordinarily ads reflect what an advertiser specified not what the platform thinks is most likely to be successfully sold to you.

mlthoughts2018 8y ago

There’s a distinction between what these companies can predict about you vs what they can roll up into products that other entities want to pay for. For example, a ton of advertisers will predetermine some targeting criteria based on simple demographics, brand loyalty or rewards program data, etc. and then commit to it for a whole ad campaign, even if it means leaving money on the table by not electing to dynamically shift into more precise targeting. For these clients, no amount of fancy predictive capability or algorithmic targeting will matter, they just don’t care. So Facebook or whomever just offers them big, sloppy and easy-to-conceptualize segments or buckets of users. They just want aggregate intelligence anyway, so nobody in the transaction cares much if it’s wrong about your age bracket or general TV interests. This doesn’t mean Facebook is unable to produce far more alarming forecasts of your behavior, or assign you to categories based on things like political activism, privacy conscientiousness, or detect personal life details like your location trail, purchases, etc. The really scary stuff just doesn’t end up being surfaced in connection to lowest common denominator adtech products.

deogeo 8y ago

What if the data is used to find whistleblowers? Or predict them? I'm sure there's plenty of corporations and governments that will be willing to pay for such services. https://www.abc.net.au/triplej/programs/hack/how-team-of-pre-teens-found-whisteblower-using-metadata/8113668 https://www.abc.net.au/triplej/programs/hack/how-team-of-pre...

darkpuma 8y ago

Something you should keep in mind is that companies like Amazon are all well aware of the anecdote about Target and teenage pregnancies. And the lesson they learned from that story is they should conceal from the user the full extent of what they know about that user, to avoid creeping people out. If you receive 10 product recommendations, perhaps only one of them is actually targeted with a very high degree of confidence and the other 9 are basically noise added to deliberately deceive you, to lead you to believe Amazon knows less about you than they really do.