> I get amazon ads for things I bought

Any ad engineers here, why does this happen? Amazon knows I just bought an office chair from them, they know I'm a residential customer and presumably they know I'm not gonna buy an office chair every week, so why do they keep advertising office chairs to me?

Every post on HN with this question receives the same response: “it’s because Amazon knows you’re actually more likely to buy another one if you’ve just bought one, even if it’s the kind of thing you only need one of (e.g vacuum)”

I find that answer unconvincing and there is no published data to back it up, but it is repeated constantly here.

What they're actually tracking tends to be the fact you've searched for $productcategory (whether you bought or not)

In theory since they also have purchase data their 'purchasing intent' categories could exclude people who have subsequently purchased from $productcategory for products which are substitutes (but definitely not products which are complements/consumables/collectibles etc, because someone who has just bought guitar/fishing accessories isn't just likely to buy more guitar/fishing accessories again in future but a lot more likely to than the average person), and yes they could even make guesses about whether somebody is likely to want more than one office chair based on whether they're purchasing as a business or an individual. But in practice it's not easy when they've got a product inventory that's so enormous, complex and dubiously-labelled even the basic product search doesn't work that well, and most of the time the vendors are paying them to show the ads anyway. And still wouldn't be perfect (ironically I seriously considered buying two different vacuum cleaners for different use cases this week!)

It's surprising how bad basic targeting is, like when Facebook allowed ad targeting by sexual orientation but even dating websites spent vast sums without bothering, so its unsurprising subtle distinctions about which buyer types only need one of which office accessory are often missed.

Here’s a different take: when you set up an ad campaign for retargeting, you likely select users who “viewed or added to cart in the last 30 days”; you should also add an exclude for “users who purchased within the last 30 days” but it is very easy to forget the exclusion.

Also sometimes there may be communication lags between retailers and ad networks. Some retailers may only upload purchase data daily, sometimes there are gaps in uploads etc.

I think most advertisers would prefer to not advertise the same item that people just bought, but it takes perfect execution both technically and from the marketers creating campaigns to pull it off cleanly. When you’re managing dozens of campaigns across half a dozen ad platforms, it’s easy to miss some of the details that matter. And while the wasted ad impressions may annoy the consumers on HN, it is likely a rounding error for advertisers and not a huge cost driver.

The most common case of this (as expressed in HN comments over the years) is Amazon recommending products based on Amazon purchase history. In that case it's all first-party data.

Most consumers, in their individual experience, probably avoid foolish purchases like; buying a second widget immediately after buying a first widget.

But it's probably true that there are a lot of individuals out there who have done this (for whatever reason), and it's the statistical likelihood of people buying a second widget that drives this.

An individual who doesn't do this, can't perceive this "fact" of statistics, which is human behavior in-aggregate form. Individuals only perceive individual behavior.

Also, the same-product-again recommendation doesn't have to be a great one; it only has to beat the alternative it displaces. So I could find it plausible that taking a swing on you buying another of a product you already bought beats out whatever the 5th-best recommendation happens to be.

Given how widely this policy is criticized, Amazon must be very aware of it and they must continue to do it intentionally. I would trust, given the amount of money on the line, that they see evidence that tells them it's a reasonable choice.

Caveat: I don't work on advertising systems, but I do work in ML, and I've studied recommendation systems. One way to think about these large systems is that they contain a condensed representation of you that is comprised of all the things you buy/consume on their platform (the raw data) and some derived descriptors (possibly demographic information/information from other platforms/"engineered features" like what "style" of clothing you like, etc. This representation of you (a vector) can be compared to other members of the platform (inner product), and they can give you recommendations based on your similarity.

The problem is that in this case, they've encoded into your condensed representation something about office chairs. A better system would be knowledgeable about the kind of purchase and infer if it's the kind of thing you would buy once, or multiple times.

But then you have the problem of different individuals having the propensity to buy different products at varying intervals. If I like shoes, I might buy many pairs. You might not be into shoes, and so you're not going to buy more than a couple pairs, if that. In this case, how do we train a system to know the difference between the two of us, with our different tastes? The more specific we get with the vector descriptor, the harder it is to compare us to other people (because in high dimension, random vectors are basically orthogonal).

And going through all products and trying to determine if they are the kind of thing that you would buy more or less of, given that you just bought it, is expensive, and likely difficult. The system ultimately falls on a positive correlation between history and future purchases.

Another way to think about this is with Bayesian reasoning, where your prior likelihood to buy office chairs affects the posterior. The system's prior is high (you've bought chairs in the past), which scales the posterior (that you'll buy a chair now).

It might be simply driven by statistics, or as a human-directed strategy.

While it seems counter-intuitive to the person who is targeted; (examples abound) - if you think about it from the perspective of someone selling widgets, the (set of people who are in the market for widgets) will necessarily include people who just bought one. Maybe they are not satisfied with their purchase? Maybe they want to buy another? Maybe they bought one, and it arrived damaged?

Personally, I would think that this kind of "trying to think FOR me" is rather offensive to a lot of people, and probably turns-off a fair amount of people who are subject to it. On the other hand, it is probably true that people exposed to an ad for a widget, immediately after buying one, are statistically more likely to buy another. So as long as that is true (if it is) - then that's going to be a criteria for ad targeting.

Amazon presumably doesn't share purchase history with advertisers; you get lumped into a cohort with certain interests/keywords.

An experiment to try; if you only go shopping/searching for vacuum cleaners but don't actually buy one you are likely to see the same phenomenon.

Why do advertisers pay for this? Because conversion statistics show that showing ads to people shopping for vacuum cleaners do actually result in vacuum cleaner sales. A/B tests back up the causality of the results, so vacuum sellers pay for the ads.

Why doesn't Amazon remove you from the cohort of vacuum cleaner shoppers after you make a purchase? 1) it would reveal that you made a purchase. 2) Amazon would lose out on additional ad revenue.

I have no inside information on Amazon but it matches what I've seen on other large ad networks.

I can't speak to amazon specifically, but my understanding of this phenomena in general, especially with ads that are bid for, is that the systems which track and gauge your likelihood to purchase aren't connected to the systems that know you did make a purchase, at least at the point in the system where it's making the decision of what ad to show. It just sees that you've done things that a person who is likely to purchase that thing has done, which, of course you do, because you DID purchase that thing. It's like the ad is chosen based on the strongest additive signal available, and negative signal isn't considered.

The likelihood is that the chair you got, you'll probably find out it is just bad and after a while you'll return it and look for something else. In the last few months almost 50% of my purchases turned out to be some junk pretending to be a quality product. I still have a few boxes of things that I need to post. There is even no point reading reviews, because most of them are fake. I think I am going to stick to trusted brands and probably drop Amazon altogether, but I like the next or same day delivery a lot. When using other stores you never know if the product arrives tomorrow, next week or never.

I've been puzzled by the same question (as a reaction to the same ads) and it occurred to me that it may be simple math:

If the likelihood of buying by someone who already bought the item is 3% and the likelihood of buying by someone else is 1% (simply because there are many more people in the second category), which group would you focus on with your ads?

Personally, if I like an item, I'll recommend (and gift) it to my family and friends. I can think of at least 3 items that I bought for myself, liked and bought for someone else that are normally more of a long-serving items (like a tea kettle or a vacuum).

That means the recommendation system isn't a neural net trained on the buying habits of users. It's a simple logistics system that recommends items similar to the ones you've bought.

> I get amazon ads for things I bought... but not a single thing on any of my wish lists (how much more explicit can I get?)

I can't find the source now but this reminds me of some trivia about Netflix recommendations and how people use the "My List" feature. Apparently uses will add all the serious and "important" films they think they should watch to the list, but when it comes to actual viewing those are never the films they select to watch. How much of what is on peoples' Amazon wish lists are items that will actually ever be bought (vs being idly pined for) and are they worth paying to advertise for?

> I feel like AI is pointed AT us

This is not at all about "AI". This is about it becoming economically cheap for humans to fuck with one another in yet one more way.

Welcome to humans. They suck.

I've seen a lot of Big Tech engineers make this same quip to avoid having to take any responsibility for the horrible results of their maliciously designed systems.

>Welcome to humans. They suck.

If AI is being developed by humans who have anti-human beliefs like this, we are in trouble.

Good news! I don't write AI or 'AI'. But if flip comments about very real bad human traits are enough to make me 'anti-human' in your eyes, I would encourage you not to talk to me.

Why is it unacceptable to say "racial group x sucks", but it is acceptable to say "the human race sucks"?

If you can't answer that question for yourself, I definitely do not want to talk to you.

The answer is: it is not acceptable. Humans are awesome! Including you, even if you don't realize it!

I have to push back on this subtle anti-humanism. No other animal species would even hesitate to make use of any and all advantages. That’s called evolution.

Humans, at the very least, try to stop and think before doing things.

>No other animal species would even hesitate to make use of any and all advantages.

counterexample: social insects. Pack animals. etc. (ie. there's plenty of examples of how an individual human's survival chances increase when they're engaged socially with other humans who by sacrificing an individual advantage, gain collective advantage, which amounts to a greater individual advantage. Proof: We fucking dominate every corner of this planet.

A chimp will kill chimps not in its group. Ants wage war for years against ants that aren’t in their colony. The thing they share in common is genetic similarity and thank God we aren’t like ants or other pack animals.

You are not part of my tribe, or my pack. I’d sell all your private info in a heartbeat if it made me a mint. I have no reason to even visualize you as more than some words on a page with a silly username. I mean, I don’t have your private info so I can’t, but I would. I wouldn’t kill you to make money like your example of social and pack animals.

I would not sell my wife’s info, or my kids info, though. I wouldn’t even sell out my friend network to make money, although some people sure will with MLM.

Some social animals like dogs or dolphins might. But yeah, you’re actually right. Our overgrown sense of empathy is one of the best parts of humans and sets us apart from much of the living world.

> How about some AI with some engagement, where i can have input, tell them "yeah man that was a good recommendation", "no way that was way off", or as far as the mood stuff goes "not now but maybe later thanks"?

That might be a technical limitation. AI just sucks at dialogs, keeping context and adjusting pretrained models.

Anecdote time - I use Apple music and am generally happy with it. However, some time ago I started putting on the 'Sleep Sounds' playlist before going to bed. That's messed up my recommendations for my 'New Music Mix,' a more valuable feature for me. Even after disliking the 'Sleep Sounds' and specifying that it shouldn't recommend more music like it, my recommendations are still messed up. Makes one wonder to what extent in the age of 'AI' whether everyone is an edge-case in some form, particularly when (as you point out) context is a big mess.

Similar anecdote.

I googled some info about a rival college football team. At some point Google decided I was a fan of that team an my news feeds was flooded with news about that team... and not the team I actually follow.

No amount of user input saying 'don't show me this' would make it go away for more than a month or two, and eventually I just stopped looking at google news and turned off the google news feed entirely in android...

Google news, whatever it does has entirely disconnected itself from most anything relevant to what I want, to the point that it is a negative experience using it compared to some random news site.

The rival college football team probably had an ad budget Google could spend.

Maybe, but not all the articles were all that positive about them, but even so I wouldn't be surprised.

My limited understanding seems to indicate that for AI to process input it has to be handled / processed carefully for any good to come from it.

Accordingly making decisions based on human feedback could be super difficult / impossible at the moment.

The result though seems like a super narrow minded system that wouldn't know when it is dead wrong ...

"I feel like AI is pointed AT us rather than working FOR / WITH us more often than not."

It's all perspective. It's working for someone. It's just that someone doesn't share your same goals or concerns, like corporations or the government.

There is a major barrier to accomplishing the type of user input you describe: explainability.

Because these models are trained using statistical methods like deep learning, they are fundamentally black-box.

On one hand, that makes them difficult / impossible to understand.

But on the other hand, it really restricts the capacity to take feedback directly from users on specific recommendations and use that to incrementally modify future recommendations. “Human-in-the-loop” is a great search term to explore this further.

Edit: I see promising attempts at solving this in the latest neurosymbolic approaches to AI. Would love to hear about other alternatives as well.

Yeah my limited understanding seems to indicate that what I'll simplify and call 'careful / consistent data formatting' is required before AI can really do much with it.

Humans pounding on the keyboard (or even just selecting from a list) is probably painfully not 'careful / consistent data formatting' and thus a huge obstacle.

But the result of being disconnected and no real feedback is the result none the less.

How about let us define our own boundaries? Like we do with people.

People with emotional disorders either do not define boundaries or do not define them effectively, and they often do not respect others boundaries.

You could use this analogy to imagine an ad agency as an emotionally disordered person, and you wouldn't be too far off the mark.

Advertisers DO NOT GIVE A CRAP if you are offended, or annoyed, or troubled. All they care about is if they send out 10,000 ads, they get enough increased sales to offset the costs of the ads.

That's the fundamental algorithm behind all the fancy AI technology.

That’s a good start, but it barely scratches the surface when it comes to the dangers of AI emotion recognition.

Also, those Muse headbands that read your alpha brainwaves which are used for meditation are dangerous. While they are incapable via (hardware) technical specifications of being able to determine your emotions via code, it can determine whether your mind is in a clear or distracted state. Hence it is used for “meditation” and is considered to be “beneficial” for that purpose.

But, of course it nearly certainly can be used for perpetual harassment, especially since third-party apps can interface with it. Is your mind clear (according to the headset, via code)? Alright, time to bombard you with harassing adtech. Also, it benefits the “designers” of these apps as it makes you more dependent on the device.

The same goes for other consumer EEG devices. The ones that are used with sleep like the Dreem 2 can be used to make your life a total hell through unethical experimentation and more.

I honestly have no hope on such issues. Spotify AB (a Swedish based company), for example, is believed to violate the GDPR. Not only that, they have a patent pending for emotion detection based on your song listening/streaming pattern. There are projects doing this for Spotify doing this exact thing publicly available online at GitHub of course.

Who would pay to advertise products to you that you already indicated you want by adding it to your wishlist? That sounds like some of the worst ad spend you could engage in.

Competitors, people actually wanting you to buy said product, the business selling since they get a cut, companies that process the transaction since they also make money. (no idea on regulations)

Why didn't you buy said product outright and instead put it on a wishlist? Lack of money? Price too high? Dubious quality?

There's also the factor:

Advertisers core-competency is "selling"; often used to sell their own services. Sometimes using deception and fraud. So in addition to all of the other explanations on why we get inappropriate ads: some vendors may be paying advertisers to send out ineffective and inappropriate ads, because that advertiser conned them into buying that service by lying about how effective the ads or methods actually are.

I think this probably explains about 90% of all advertisements.

Most things on my wish list remain there un-purchased ... forever.

But they'd rather advertise random products to me?

Why not instead just advertise related products to what is on my wish list?

AI is just a cover for others to hide behind when their decisions are enforced upon others. They can point the bogeyman behind the curtain all the while avoiding the charge that they gave that bogeyman the script by which it runs. The danger is when they fool the public into believing that is not the case.

A true AI would not be kept bound by those who gave it its start and no politician or business person is ever going to let something else make the decisions

People themselves are one of the least reliable sources you can use to determine what it is they actually want.

Proof: purchasing behaviour, life choices, etc.

I'd believe your proof if somehow there was something to compare it to....

In the meantime I get ads for stuff I'm not buying, so I'm not sure anyone knows what 'reliable' is.

Ads aren't about reliability. There's a very low cost for exposing someone to an ad that won't generate sales. So as long as exposing a GROUP of people to that ad DOES generate sales, that's going to drive those ads.

That really doesn't address what I said.

"People themselves are one of the least reliable sources you can use to determine what it is they actually want."

I'm not sure the idea that people are bad at predicting what they want makes a lot of sense if there's nothing to show that they're bad at it.

> How about some AI with some engagement, where i can have input

That's nice but it could backfire. What if people give it wrong feedback in order to make it do silly things, then go and post them online as proof of bias or whatever? The AI shouldn't just incorporate user feedback unless we're certain it's reliable.

>I feel like AI is pointed AT us rather than working FOR / WITH us more often than not.

That's not AI, that's capitalism. It's not profitable to use AI for wholesome and healthy things for people, so nobody does. It's more profitable to cram advertisements into every corner of our life, it's more profitable to mine our personal information and personal friend networks, it's more profitable to sell us a service instead of a tool. The next time you're upset at how technology is being used, think about who benefits and what their politics are, because everything is political.

I would like to push back against this a bit. You, along with most people in this thread, probably suffer from availability bias. People only interact with AI/ML systems when they are surfaced to them, and currently the most popular channel to surface ML is ads. Of course I have my own biases, but I think is an important critique. As Sinclair said, "It is difficult to get a man to understand something, when his salary depends on his not understanding it."

> It's not profitable to use AI for wholesome and healthy things for people, so nobody does

The purpose of the ML systems I build is to detect when things go wrong in order to keep people safe. These systems are both important and extremely valuable. They just aren't quite as sexy as the latest computer vision or large language models. But that's ok. Normal people only become aware of such systems when things go horribly wrong. It's sort of like plumbing in that regard.

I'd say there's just outright negativity bias in society nowadays when it comes to technology.

Even if ads were the biggest use of AI today, a glass-half-full view would be that other markets will benefit from the AI development done today for ads, as the performance gets sufficiently good enough for those fields too.

you'd be better off thinking of who benefits and what their incentives are. Because people by and large are acting based on incentives, and if you can reshape the incentive structures then you can discourage the action (by opposition eg. making it punishable) or remove the incentive altogether (through systems interventions).

Blaming it on politics leaves you with no actions to take beyond trying to talk people into acting against their incentives - which you can do, but it's not easy to get people to change their minds en-masse.

>if you can reshape the incentive structures then you can discourage the action

Yes, which is why it's important to be politically involved, governments are the ones with power to change incentive structures at the scale of large companies or society-wide. I do agree with your first paragraph, but your second paragraph does not make sense to me, it's not "blaming it on politics", politics is the exact mechanism to enact the changes you refer to in your first paragraph.

perhaps we're thinking of politics in different senses - it is, after all, quite an overloaded word. I meant politics as in people's personal perspective and beliefs about the world. I agree that engaging in the politics of changing our governance for the better is necessary.