I just spent 15m on Amazon trying to prod the recommendation algorithm into finding something I actually wanted to buy so I could get above the "free delivery threshold".

Think about that. I wanted to spend money. I wasn't too fussy what it was. Amazon has a decade of my purchasing and browsing history.

And they still failed.

Amazon infuriates me. I regularly buy gadget-y bits like electronic components and peripherals. Probably at least once a month. Never see adverts for similar.

That ONE TIME I buy a unicorn dress for my 2 year old daughter? That's a lifetime of unicorn related merchandise adverts and recommendations for you!

Actually that's really interesting because it exposes a bias in their recommendation system. They must be heavily biased towards things with mass appeal instead of specifically targeting user preferences, which is funny because it goes against the grain of the whole "targeted advertising" promise of ML. You'd think if anyone could get that right, it would be Amazon, yet..

It's not impossible for that to be a clever decision by Amazon (although I'm not saying it's likely, I have no idea about the numbers).

The ultimate goal of the advertising is return on investment, not making you feel interested in the adverts. If, to exaggerate the possibility, 100% of "people who look at tech" are 0% influenced by adverts, but 10% of "people who bought a unicorn thing" will go on to buy another if they're constantly reminded that whoever they bought it for likes unicorns, all of a sudden it would make sense despite being counterintuitive to viewers.

A more commonly discussed example of a similar thing is that it's easy to think "I just bought a (dishwasher, keyboard, etc), I obviously already have one so why am I seeing adverts for them?" Sure, it might be that the company responsible has an incomplete profile and doesn't know you bought one already. But it's also possible that the % of people who just bought the item and then decide they don't like it, return it and buy a different type is high enough to be worth advertising to them.

This is basically what comes from a mindset increasingly common among ML practitioners of abdicating thinking and assuming "the machine will find what features are important". They throw a junkyard full of features at the algorithm (or even worse automated feature generation). These days at least once a fortnight I get the opportunity to show folks how if only they thought for 10-15 mins or simply charted their data in a few different cuts before modeling, how much better they could have done :-)

I suspect Amazon has learned that some feature labels are easy to recognize and correlate (dress color, size, style, etc) and others are hard and lead to useless results (an electronic device's: computer, format, port type, protocol, etc).

So they gave up trying to match CPU with GPU and went back to connecting beer to diapers.

Ha. Last time I wanted to buy something on Amazon, their search page kept freezing every time I load it: 100% CPU load. Because I really wanted to buy that thing, I spent 30 mins debugging their silly scripts and found one for-loop that tries to find a non-existent element. Unfortunately, I couldn't figure out a way to enable my fix in a minified script, as reloading the page kept loading the original script.

Put a breakpoint on the offending line, manually add the element when it breaks, hit resume and hope for the best?

Whit uMatrix, you could have created a rule to block that script.

Turn off Javascript - Amazon still works OK.

Amazon still suffers from those "This guy really loves washing machines!" type recommendations

I hope 'm' here is minutes, not millions )

don't gift card count anymore?

Right? Recommenders are almost counter productive for me in most cases. I want a recommender to remain broad, not give me increasingly niche recommendations a la youtube. About the only halfway decent recommender system I've interacted with is the various forms of curated lists on Spotify. They seem to actually take a decent stab at it with related but sufficiently different and interesting content.

For all Facebook knows about me they've always been exceptionally bad at advertising to me, which is remarkable considering what they've got. Google is only very marginally better. Actually, now that I think about it, Amazon's 'customer's also bought' is also pretty bad at the recommendation itself since it not uncommonly recommends incompatible things! ...but it does often succeed at getting me to think more about what else I might need and sometimes leads me to buying other things. At least it's not always recommending the same thing, but rather related things, which is probably a much better way to advertise.

What I've always assumed, but now this thread has me doubting myself, but what I've assumed is that these systems, even though they appear to suck at specifically targeting me, must somehow be pretty good on the average, still netting big profits overall even though they don't seem to live up to the promise of getting me to buy stuff. But if everyone has this impression, maybe it doesn't work? I mean, I assume companies like Amazon and Facebook would be pretty good at least at optimizing the total rewards from using recommendation systems, but maybe you can't tell from your own anecdotal use. I have no idea. I'd love to see an analysis that includes aggregate numbers.

I think you've got the nail on the head. It's sort of the tragedy of the commons, which in terms of recommenders is a really easy thing to accidentally optimize for. For example, pop music is popular. In the early days, recommenders would just recommend pop music because on average that was a decent recommendation. We've come a long ways since then. Well, everyone but Facebook has.

> For all Facebook knows about me they've always been exceptionally bad at advertising to me

Because it's not Facebook really, it's the advertisers who choose targeting criteria. You as an advertiser have a myriad of options. For example if you've built a competitor to X, you can target users who've visited X recently, aged N-M, residing in countries A, B and C, and so on. There are options with broader interests too. Poorly targeted ad means poorly selected criteria by the advertiser (or sometimes just advertiser experimenting) and consequently money wasted. Facebook doesn't care though.

Then there is retargeting/remarketing (target bounced traffic) already mentioned here is probably the stupidest looking invention that actually works.

But they are also really really bad at curating content I like to see. Like even worse than the advertising actually. My feed is just garbage and has been ever since they switched over from being chronological. But I sometimes get on a wild hair about some particular person, and I'll look at their page directly and see actually interesting content on their page that was never shown to me. Facebook's only real guess is then to just say: oh! You must like this person, let me show them, all the time.". The reality is, I'm interested in stuff like when a biased person I don't like shares a more neutral or inclusive opinion, or someone does something interesting. Facebook is just unuseably bad at selecting for that. Their algorithm just pushes really shallow crap and buries anything with substance or depth. I keep it around just to stay in touch with hard to contact people.

Likewise Pandora does a decent job of picking songs for me based on what I previously liked. It's not perfect but far better than random.

it's easier to recomment things you probably like than things they want you to like/may like

Hey, personal experience and to be fair for them, Google does occasionally give me ads about a Haas CNC machine. I really want one. But I don't have the disposable $100k for one and I don't have the space... nor do I have 3 phase power. But I do want one and haven't bought one. So, good on them, right?

This can happen when a retargeting campaign doesn't have a 'burn pixel' or conversion event trigger. It's a common oversight, which can cause a re-targeting program to kick-off unnecessarily (or cause ads that have followed you around to become obvious)

You'd think with all the "AI" out there, they could match a sales DB entry with the CRM DB entry, but, in fact, they basically can't.

The AI innovation must be that they can figure out which marketers are likely to forget to have a burn pixel because those marketers drive more revenue.

And yet I haven't seen one ad that has a 'not interested' feature similar to YouTube.

I mean, if I could stop seeing washing machines or whatever, I'd probably click it.

do you clear your cookies and html5 storage? That should wipe any personalization that's happening, but the ads will become very generic then.

You can also block ads (Ublock/Umatrix)

Oh yeah I can do that, I'm just wondering why they're not too interested in direct user feedback from my end. Surely it would be useful to adjust their algos.

That's because:

* They don't have suppression set up

* They're using a conversion tracking platform that is slow

* They're testing the returns conversion hypothesis: you have expressed concrete intent, you have bought the product. If it has 5% return rate, you probably still want it, and there's a 5% chance they need to be in the mix.

Not to mention these systems are also completely useless at recognizing one product someone bought already includes the product they're recommending. When you buy Dark Souls 2 with all DLCs you can be sure Steam will suggest Dark Souls 2 without any DLCs to you for at least a full year.

Sometimes it also recommends other brands of the thing I just bought. Like a cell phone: I didn't buy Samsung, but now I see their ads. I guess that is a very very small improvement- Maybe I'm the type of consumer who gets a new phone every week?

No, but you might be unhappy with your phone, return it and buy a new one.

You are luckier than me: I've spent 10 years bombarded with ads for things I never bought, never wanted to buy and that are directly insulting at this point. (Buying something would maybe give me a two day break though.)

I have been looking for a shelf that’s as close to 50” wide and 10” deep as possible. No search on any site that allows you to search for such a thing as far as I can tell.

You can do this at wayfair.com. It's kind of hidden though, when you get to the shelves, click "Sort & Filter" and then scroll down until you see the dimension sliders.

I think amazon works pretty well.

They show me similar things to the thing I just put in my cart. Sometimes better choices that I made.

They also show related things that other people have bought, that many times I end up purchasing.

Dirty secret: "The customers who bought also bought" algorithm doesn't require ML/AI.

You can accomplish that with relational algebra on a precomputed data warehouse job and only for products with strong correlation. The intelligence of the customers is enough agency to instil a semblance of intelligence in the data.

Yes, this is the straightforward ‘collaborative filtering’ algorithm. I suppose the line between ‘algorithm’ and AI/ML is not well defined though. At what point does a technique become ‘AI’? I don’t know a good answer.

As an utterly cynical layperson, algorithm means directly querying data. AI means feeding systems with training data and sprinkling them with magic obfuscation dust.

I wonder if the dirty secret for lots of stuff is: "They aren't using AI anywhere"

Well, for problems that are simply looking for a ranked relationship if you have human input it could be used to train a ML that attempts to look for similar correlations...or you could just use your human mechanical turks that are already informing you. ML problems that are good are the ones not trying to approximate reality like CWBAB.

I'm doubtful we'll see an ai that makes a serious jump without directly interacting with the world we live in. Under that measure cars might be the closest since you learn to interact with the bounds of where it can and can't go is similar to a toddler learning to crawl.

The marketing budgets are there to be spent :)

Yes - just in case you want two or stopped the purchase along the way.