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Amazon Personalize: Real-Time Recommendation (amazon.com)
199 points by davidjnelson 56 days ago | hide | past | web | favorite | 119 comments



The democratizing march of technology. Once the exclusive domain of tech giants, now even small businesses can integrate this service and attempt to sell fridges to customers who have recently bought a fridge.


What are you talking about? You just searched for an SNL skit. Are you telling me you don't want to now watch every single SNL episode ever made as well as join an improv troupe?!


That was Interest-based advertising several years ago (or at least the last time I surged around without an ad blocker). I found it ham-fisted. However - the interesting thing that comes out of this is that if someone buys, say, chocolate and marshmallows, there’s a reasonable chance they want to buy graham crackers. So why not offer them graham crackers at checkout if it’s not in their cart?

More insidiously - why not offer a higher price on graham crackers if they seek them out on their own? They’ve already made a decision.


To take it a bit further, you'd probably want to mark down the checkout suggestion to not only make it look better, but also because you're only suggesting a very limited amount of options, once customers are used to trusting the checkout suggestions for a good deal, you introduce higher-margin and/or promoted products.

More or less the same tactic Amazon used for search results: Return useful/functional results for a few years to train you users to trust what is near the top, then start putting promoted products at or near the top and generally ordering the search by what increases your revenue the most, rather than for any user-oriented goal, AKA "sort by relevance".

Also the same thing Amazon did with prices in general, get people used to thinking you're the market with the best price/deal, etc.


Amazon got massive backlash in 2001 for messing with prices during shopping experience. Some companies do it, but customers and news media hate being toyed with, and loss aversion bias means they tolerate "wait! Before you go!" discounts but not price increases.


What? You don't want a baker's dozen more stud finders? I thought you loved them!


Honestly using Amazon is like tip toeing around a lion to get something valuable. Knowing that at any moment they could wake up and and absolutely swallow you whole.

If you use their cloud products, ala Netflix, they may decide to compete with you any moment. Netflix may have been too far along to be beat but the next product won’t be. If you sell products on their market place and if they do well you run the risk of them copying you. If you do anything in their ecosystem you are living with the fear that the lion may wake up and swallow you whole at any point and time.

Edit: Maybe I’m a bit paranoid but this feels like a great way for them to get to know exactly who your customers are and what their behavior is. All the while training their own personalization to get much better.


They also like to buy shares in smaller companies or outright acquire them. Not always competition.


Right, like with the YC startup Scaphold turning into aws appsync:

https://www.linkedin.com/in/michaelparis1/


Candidate for business school case study, what factors influenced Amazon decisions to invest/acquire vs clone?


sorry if this is a stupid question, why would amazon only copy things hosted/using aws?


AWS can see traffic and usage. They can predict the "next big thing". Facebook allegedly did the same with their VPN services that analyzed what apps people were using on their phones.


AWS knows more precise numbers than the public, and they may know an app or a service is really popular before it crests in the public zeitgeist, but I don't believe they would know earlier enough than the rest of the public to make competitive product roadmap decisions.


Does Amazon allow human employees to review the analytics of client sites? If I was a big customer like Netflix, I would demand that a restriction like this be written into the contract.

Of course, Amazon could lie, but now if Netflix has a reason to suspect wrongdoing, they could sue.


Not that I disbelieve you, but it would be interesting to see a source for that claim.


The Wikipedia article about Onavo[0] has multiple articles linked about it.

[0]: https://en.m.wikipedia.org/wiki/Onavo


There's probably no way for me to convince you otherwise, but I'll say it anyway:

This isn't how the world works.


Gathering intelligence and moving into their partners/supply chains territory is literally the DNA of Amazon. I know of no other company that does it as verociously as Amazon, who went after every partner that helped them grow (fulfilment, delivery, third party sellers, manufacturers).

And the example of Facebook with Onavo is also not plucked out of thin air[0].

Sure, it's not how all of the world works but for the big players, it's a important tool in their toolbox.

[0]: https://en.m.wikipedia.org/wiki/Onavo


"verociously": a portmanteau of "ferociously" and "voraciously". ;)


Whoops! I'll stand by it :D


Industrial espionage is a real thing, if you believe it or not.


> Edit: Maybe I’m a bit paranoid but this feels like a great way for them to get to know exactly who your customers are and what their behavior is.

You are not paranoid. That's exactly the business model of the retail website when hosting independent merchants.

The lion waits until you finished eating dinner...


Many of these commentors have it backwards. I know this because I’m actively working on a similar project at AWS. It isn’t that Amazon wants your data to help it make decisions, so it builds a clever product to get all your data. On the contrary, Amazon has so much damned data and years of experience turning that into a valuable asset, that it has started experimenting with ways to sell that value to other customers. The idea that it wants some mom and pops retail data is just silly. Our biggest problems are scaling the internal services we built 5 years ago to handle all the growth.

SageMaker was a ground floor service and you should expect to see many more services built atop it.


People here mostly appear to be criticizing the quality of the algorithm, not the fact that Amazon wants the data.


so is this the equivalent of software as a service?


I mean, isn't that half of what AWS sells?


Amazon’s years of experience usually end up recommending you the same thing you just bought or just glanced at and never came back to the same category


If you could solve the problem better, at scale, I’m sure they would pay you a lot of money to come solve it. Along with Netflix, Google, and all the other places that use standard algorithms designed to solve these sorts of problems.


Netflix actually did pay contest participants a lot of money ($1MM) to solve the problem better, at scale. But they didn't end up using the resulting algorithms.

https://www.wired.com/2012/04/netflix-prize-costs/


I probably wouldn't pass the interviews at most of these places :-P

But if they pay me (well the consulting company I work for) enough I might fly in to hold some session along the lines of

- don't annoy power users for no good reason (a certain ad delivery company with an attached search engine might be interested in this)

- how to please all your customers at once - the forgotten art of making your software configurable. Bonus session: sensible defaults (audience: every modern software company)

- your customers might not be as smart as you but most of them aren't braindead - and other notes from the field (same audience)

- continue to be the best - by not nerfing your almost-perfect product (same audience)

- not storing data you don't need to serve your users [0] - a practical guide to not getting fined by EU while also getting rid of dumb disclaimers that won't help you anyway when regulators get fed up (audience: everyone who has a GDPR popup with default opt in to one or more likely more than 100 different trackers)

- why customers who have already bought a dishwasher won't buy a new one however much you advertise for them the rest of the month (and other great secrets from marketing 101). (Audience: most adtech companies and ad buyers)

- the long-term benefits of serving ads that actually provides value - or why you'd want to sell some ads to other companies than scammy dating sites even if they are currently the easiest ones to fleece by showing expensive ads to customers who aren't interested at all. (Ad-tech companies, and that big one with the attached search engine in particular.)

Master class:

- 1/3 Living in a global world: Europeans are also on the Internet and knows when movie and series releases are + other shocking truths.

- 2/3 increasing digital sales by putting the content up for sale - how to increase sales and customer happines all while reducing piracy just buy removing stupid geo limiting

- 3/3 the synergistic effects of making your content available to customers who wants to pay for it while it is still hot

[0]: Deliberately talking about users, not customers here to avoid the ever-helpful response of "if you are not paying you are not the customer, you are the product". And BTW, I agree, it just isn't always helpful.


Your issues with geo limiting around content are almost entirely an artifact of the publishing systems. Having had to work on those things, amazon does not actually want to waste time implementing them except that it's a requirement to get the content people want to buy. Put the blame where it belongs.


That part was about certain media/entertainment companies :-)


Then what does it have to do with recommendations?


It doesn't have to do just with recommandations but more generally all the big issues that the biggest companies have to deal with ;-)


I'm fairly sure that ignorance isn't the reason these companies aren't implementing your suggestions.


Well, I don't expect a sales boom but I struggle to come up with a better explanation for a couple of those.


It seems odd though to suggest the same item I just bought over and over again.

Same as YouTube's algo now recommending already watched videos, over and over.

This is either being gamed by bots or someone at those cos is not dogfooding. And if all else fails, for the love of god, give me an option to train your dumb algo manually by blocking shit I don't want to see ever again.


Sometimes it makes sense, for consumable goods or even non consumable things like storage containers, where you might buy more of the same item at different times. Other things like books I agree don't make sense.


> give me an option to train your dumb algo manually by blocking shit I don't want to see ever again.

Feel free to recommend my course posted below :-P

"how to please all your customers at once - the forgotten art of making your software configurable"


Yeah YouTube's algo has become even more terrible, which I didn't think was possible.


Or another way to look at this is that if the problem can't be solved well - in a way that actually adds value - then maybe don't offer it at all.

I don't believe amazon's recommendation engine has ever delivered any value to me, and it would be really nice to just have all that crap removed.


This assumes internal politics are a meritocracy with appropriate levels of prototype funding.

I’m not sure that’s accurate — and there’s plenty of ways for organizational structure to strangle good ideas.


introduce temporal component into your prediction model, similar to this:

[0] https://en.wikipedia.org/wiki/Hierarchical_temporal_memory


besides finding it odd how they select what to recommend me, including stuff I rated poorly that comes back up, the "See something new, every day" bar that displays shows item thumbnails that are not actually of items you will see if you click there.

so while they do somethings well there are just some odd choices made with how they present product. oh, with regards to that "see something new", reported it and got back that those thumbnails were not meant to represent actual products. call me confused


This is very true. I'm forever being recommended things I searched for once for a parent or a friend that I have no other interest in.


If you were CEO of Amazon, what would you do? As much h as Amazon would love to have you but something you don't really need/want every day, it's not going to happen in substantial numbers. But they need something to keep brand awareness so you remember Amazon is the place to go for everything. Even complaining about the ads is still word of mouth for the store. Even of you don't like the recommendations, talking about Amazon is t bad PR for shoppers.


If people stopped buying things they just bought, it would probably stop recommending them.


I don't know about anyone else, but Amazon.com's recommendations for me are laughably bad. "You bought a 48-pack of AA batteries? Your recommendations for the next few weeks will only be batteries, since you are clearly a battery collector!"

I don't remember ever looking at their recommendations and finding them useful. Why would anyone want to use the same algorithms?


Came here to say this. I've been an Amazon customer for a very long time (2003). I don't think I have ever bought anything from one of their recommendations, that are, as you say, only based on your most recent purchases and that assume, for some reason, that you need another version of what you just bought.

If I just bought a big pack of batteries I won't be needing batteries for a long time; and they should know this because they know that I buy batteries every 18 months or so.

Also, I don't have a dog, I have never bought anything pet related on Amazon (or any other website for that matter) and yet they keep suggesting pet food.

Amazon recommendation engine is abysmally bad. Picking a random item from my orders history would be just as good and so much simpler to implement.


Off topic, but if you need to buy batteries each 18 months, I hihgly recommend you rechargeables.


When making this switch its best to commit. Spend $40 on a good recharger then buy 64 rechargeable AA batteries. I did the same about eight years ago and couldn’t believe how quickly my cache of batteries got eaten up by random devices and accessories and toys. I actually had to buy more, BUT! the change was complete and I didn’t need to buy alkalines anymore (till my kiddo came down with a case of type 1 diabetes and we needed them for her insulin pump). Everything else gets eneloops and the ones I bought back then still seem to be going strong.


> You bought a 48-pack of AA batteries? Your recommendations for the next few weeks will only be batteries, since you are clearly a battery collector!"

Patrick explained it why it might be like so: https://mobile.twitter.com/patio11/status/982208307057246209 (thread: https://threadreaderapp.com/thread/982208307057246209.html )

> I don't remember ever looking at their recommendations and finding them useful.

Amazon does various forms of recommendations (no way limited to just recent purchases) based on

1. Purchases

2. Searches

3. Browsing history (via trackers, ads, affiliate links)

4. Activities (on their digital devices, tagging things on their websites in wishlist, adding preferences in your account etc)

5. Global and Local trends (people in your area/country buy..., people with similar buying pattern buy..., It's Eid and you're Muslim, so you buy...)

...And probably many other signals I might be missing.

They must have got pretty good people working with this and enough data to get their models right.

Also, Amazon makes decisions on new retail businesses to start depending on those signals. https://techcrunch.com/2016/11/03/amazons-private-label-bran...

That, and considering the fact that Amazon has an unshakeable culture of making data-driven decisions for everything it does...

I guess what I'm trying to say is, Amazon and its subsidiaries may not be as dumb at AI/ML as it might seem to be by gauging against one datapoint.

Also see: https://www.cnbc.com/2014/04/09/big-data-knows-youre-pregnan...


Patrick's explanation is that because of buyer's remorse, and because if a thing is going to break, it often breaks early in its expected lifetime, people are more likely to buy an item (e.g. a refrigerator) soon after buying another one than they are to buy the item in general.

Okay, that makes sense to me. They go from 0.02% likelihood of wanting to buy a fridge to, let's say, 2% likelihood. A marvelous leap!

But even though they are now much more likely to buy another fridge than the average person ... I would think that they would be more likely still to buy other types of products than to buy another refrigerator.

Even if their likelihood of needing to buy more consumable products (e.g. deodorant) is only 5%, that's still double the likelihood of their likelihood to buy a fridge again.

So I would think that there would be some bias toward more frequently purchased items, even if your likelihood of buying a less frequently purchased item does indeed increase after purchase.

I'm sure the math checks out somehow, and that the creators of the recommendations algorithm wouldn't be pursuing strategies that don't work, but I don't think Patrick's explanation fully captures it.


The revenue on the refrigerator to Amazon is 10x or more higher than the deodorant.


That seems to miss the fact that if you buy a fridge on Amazon and decide to return it, you are most likely going to look for another fridge on Amazon regardless of whether Amazon puts fridges in the recommendation section or not, since you already know you need a fridge and that Amazon sells fridges.

My only explanations are that that kind of thing happens when they can't figure out anything good to recommend.


> They must have got pretty good people working with this and enough data to get their models right.

You seem to be taking this an an axiom and running with it. My impression too, is that the recommendations are pretty shoddy.


I worked previously at a recommendation systems company where we also generated this sort of recommendation. It looked incredibly shoddy to us too, but what we found out was:

- yes, people who just bought a lot of batteries are actually more likely to buy more batteries soon. same for people who just bought a fridge.

- these recommendations may cause some sort of "burn out", where people stop looking, responding, or decide to unsubscribe. But this wasn't much worse than other recommendations. including the awesome ones.

- the loss from burnout was greatly outpaced by the wins in actually targeting the right users

Yes, these are bottom-of-the-barrel recommendations. But they work. And people don't care that much even if they disagree. Maths said so.

I think this is a similar story to the complains on non-literal searches in search engines. People think they want one thing, but they want something else.


They should.

Perhaps they are not.

Perhaps the best they can do, is to recommend to all of us the same/similiar product again because thats the only thing they got working: Catching those x percent of people who send the tv back and buy the second recommended one.


I tend to agree regarding the home page content, but I’ve found the “customers also bought” sections quite helpful lately, especially when looking at industrial/technical products.


Unfortunately the customers also bought section is not really "recommendations" as much as a table join.


That is a rather simplistic view of it


Its not my view, that's what it is. No machine learning is necessary to generate that section. Just a lot of intersected purchases from a huge amount of customers, sorted by frequency.


Do you work at Amazon, so you know this is how it works? Or are you projecting how you imagine it works?

Because if I'm an Amazon dev I'm 100% not just showing a 45 year old woman who buys a TV the same "other people bought" as a 25 year old man. I'd definitely run the same ML on each section, because the whole point is to understand the closeness of connections between personal traits and purchases, and it is impossible for a human to outperform in that task.


I used to work at Amazon. Demographics aren't needed when you have a ton of customers for which to take the intersection, and sort by weighted (frequency, time of purchase.) Any non related purchases won't show up nearly as often or close in purchase date as related ones on a large sample of customers. This is just a consequence of having big data, which makes the noise floor insignificant. ML shouldn't be thrown at everything indiscriminately.


This was the collaborative filtering of 15 years ago. It's likely they have made improvements since then.


I also find it a much more helpful section than 'recommendations'.

Something simpler is more effective in this case, in that it gives me much more relevant results. Why is that unfortunate?


I'm really surprised to see this sentiment echo'd so much on here. I find Amazon's suggestions for me to be incredibly good, though mostly with respect to books. Do other people feel their book recommendations are excellent, but not so much for other products? Am I alone in this?


The book recommendations are good.

Everything else sucks though.


I suspect that's because it's fueled more by people than pure algo, as in take into account what other people with this book have in their library etc.


Perhaps you're a dev working for an ecommerce company, and your CEO has just sent an email down the line saying "hey, Amazon has recommendations and they're a successful shop, so we should have recommendations too!" - and you want the simplest way to get the job done and move onto something more useful.

Or perhaps you think they might work well for your particular sales domain. They clearly don't work very well for product categories with lots of different items which fulfil the same purpose (e.g. one thousand kettles, of which you only need one, or one thousand TVs) but they might work great for funny slogan T-shirts or for board games.


  "hey, Amazon has recommendations and they're a successful shop, so we should have recommendations too!" 
Sounds like Groupon, who sends me 10 emails a week pushing Smog Check vouchers... which I need once every 24 months at most.


Several e-commerce and logistics companies avoid using AWS/anything amazon for the simple reason of not giving more dollars to their competitor. I have seen this in my current and last company with this as stated reason.


I have a Kindle. I read mostly hard science fiction and popular science. However, Amazon’s recommendation thing always shows me women’s romance novels, Danish crime and period dramas.


I think this is because people who buy batteries are likely to buy them again soon, at least more likely than any other product. Maybe you bought the wrong type, maybe you need more than you thought?

I read a good post about this using washing machines as an example but I can't find it.


They have all the data. They should be able to do better than this.


My theory regarding this it's the following: I helped for a while in a family related retail business and there was a lot of need for consumable products like pens/papers etc or just barcode scanners/toners and so on...

Recommending for similar products at the end of a purchase raised awareness towards those items for the next inevitable batches. I believe amazon wants its own brands to win or otherwise a strong competition among others producers.

I can't believe it's just math.


My personal favourite was when my partner bought a book on breastfeeding through my account, and for weeks afterwards my recommendations were full of erotic novels.


I think the main problem comes when you already bought them...

I mean, I understand Amazon shows me a washing machine while I'm searching for one and I clicked on several without buying, but not when I already bought one! It's not something I would buy every other week...


I am constantly recommended MAGA Hats, it started out as silly but now it's very annoying.


Amazon indeed seem to be very proud of their "more of the same" algorithm. It occasionally recommends me useful books but they seem to mix in a lot of stuff that they are just looking to sell opportunistically. They also inexplicably include products that I've bought already; from them. Also they seem confused about things like book series. Why would I buy part 8 of 30 books where I have never shown any interest in either the author or indeed parts 1-7. I regularly use them to check reviews for things I'm interested in. I quite often end up buying them elsewhere; or not at all.

Regardless, this could be a useful service for some but I do think you would need to worry about things like GDPR if you are planning to offer this to amazon. I'm assuming they've thought this through and are providing ways for companies to use this without getting in trouble. For the same reason, I'm pretty sure that Amazon is going to be very careful not exposing themselves to legal trouble here as that could become very expensive for them. So, I'm not so concerned with them trying to grab the data for their own purposes.


Other customers who bought 'AA Batteries one third gross' also bought: "Long Dong Silver vibrator" "Jewel Cannabis Delivery Inhaler" ...


Showing you them is indeed more profitable. That's why they show it.


If AWS was ever found to be "leaking" information to Amazon the retailer/logistics company from the services that it sells, it would be the end of AWS.

They are certified under HIPAA and PCI/DSS in terms of their isolation of customers data from other customers and their infrastructure management.

Anyone who thinks they would deliberately attempt to steal customer data is beyond paranoia.


Paranoid or not, why would I trust a potential competitor with the centralization and management of all my information?


The reason to be cautious is because they could raise prices or cancel your contract at their will. But as long as you are able to change providers by the time the current contract expires (which can be several years for large corporate clients), you're fine.

A reason not to use AWS as a competitor of Amazon is that you wouldn't want to give them cash they can invest in their other business lines.


What might happen is AWS might reveal "insights" or KPIs that might help its parent company Amazon, though I think it is highly unlikely since Amazon and AWS are in different businesses (as of now at least... as they converge around AI for Alexa/Robotics/Logistics, things might get interesting). It happened to 3p sellers on Amazon whose sales performance were indicators for Amazon to start AmazonBasics and other private-label offerings specifically targeted toward regions, market segments, and products with none of the downsides (survey the market, popularity index, demand forecasting, demographics) and all of the upsides (like getting preferential treatment in search result, upselling via various Amazon channels, homepage placement etc).


Haven't supermarkets been doing this for ages? For example noticing that more people are buying gluten free pasta that costs 3x the price of normal pasta and then deciding putting out their own brand GF pasta that's just 2x the price.

With a bit of effort anyone can find products on Amazon that sell well (there's services that will do this) and then try to compete with those products. Amazon undoubtedly has more insights than those services provide, but that's probably not what makes or breaks their Basics products.

Their strength is probably their manufacturing costs being much lower than the average 3p startup seller and the brand itself.

I don't know why by I love these Basics lines that are being produced by Amazon, Zalando etc. They're positioned perfectly between "brands that are overpriced" and "Chinese brands selling probably crap products with probably fake reviews". I have Amazon basics towels, camera tripod, water filter, toiletry bag and more.


> With a bit of effort anyone can find products on Amazon that sell well (there's services that will do this) and then try to compete with those products. Amazon undoubtedly has more insights than those services provide, but that's

The fact that they can make sure it makes is prominent in any search result in the category, even for a competing product, is what makes it, combined with the fact that it's the one thing on Amazon you can be 100% sure isn't counterfeit, size no one else can sell it so there is no risk of commingling. It's nice that Amazon has found a way to trade on the unreliability of their own marketplace, and to leverage dominant market power in online into marketshare in any particular product market they choose.

Great for Amazon, that is; not so sure it's good for anyone else.


Worse, by monitoring transactions, they know exactly how shitty (er, "Basic") a product can be before returns outweigh sales, without paying the cost of experimentation with their own manufacturing.


I think it's fine to be paranoid about such things, I personally wouldn't give all my data to Amazon in this way and the services terms and conditions can easily change.


Funny you mention AWS, because the strategy you describe is exactly how the Amazon Basics line is built, by usurping marketplace sellers.


I'll get right on using this the instant I want to bombard my users with 20 recommendations for a product they already own. Is there a bit of a lack of knowledge of how their recommendation system is perceived by the general public or is this something more interesting/explicable by corporate politics?


I haven't worked at Amazon for several years, but back when I was still there, there was a massive drive to find existing technologies which could be turned into AWS services - I guess part of trying to sell the AWS ecosystem as a uniquely full-featured package of services.

I worked with Amazon recs on one of my teams. We all complained about how inane the recommendations could be on a regular basis. So I doubt it's a perception issue.


I have the same thing, but I rarely use these webshops; don't the recommendations diversify if your shopping behaviour diversifies? I mean if you only order a pack of AA batteries directly as mentioned in another thread, the system only has that to go on.

Of course, they could opt to not show recommendations due to a lack of data or pad it with what's currently popular with other customers.


I mean, I'd imagine that for some products (say, movies) more of the same might be a viable strategy.


I hope they're giving it away, because their recommendation system sucks.


Is that based on data or your personal perception?


I don't know anyone who thinks Amazon recommendations are any good. They recommend the same product after I already have purchased it like 90% of the time. And I'm not talking about batteries or cosmetics that one might buy monthly.


Do we really think that Amazon doesn't have the talent and ability to tweak their models and do A/B testing to see what provides the highest return?

Maybe enough people return their fridge right after and buy a new one, or are flipping houses and need to buy multiple washing machines, or are moving into a house with more rooms and new multiple TVs, or decide to buy a mattress as a gift for their parents after buying one for themselves, or any other of the hundreds of events that some exceptional to you or I but happen frequently enough in the aggregate to actually make it a normal enough occurrence to still recommend items that are usually one off purchases.

Or maybe one of the most valuable companies in the world that has gotten to that point largely off of selling us crap is too stupid to realize that people don't usually buy 3 fridges or is unable to hire the talent to tweak the models to reflect this sort of behavior. I've got no firsthand knowledge, but I feel like the former is more likely than the latter.


> Do we really think that Amazon doesn't have the talent and ability to tweak their models and do A/B testing to see what provides the highest return?

A/B testing a bad model does nothing. I'm sure Amazon's recommendations are the "best they've got" given their tech. Which sucks.

Google runs a massive recommendation system at scale: their ad placement product (in Search and elsewhere). Google's recommendation tech is astoundingly better than the equivalent at Amazon, rapidly putting the most profitable ads in front of the best potential customers.

That said, Google will never release their ad tech recommendation system "as a service", since it would kill their golden goose. That whole part of their company is a black hole of information.


Literally handing over your most valuable customer data to a current or future competitor. Surely only small time ecommerce operations in niche industries would dare use this.


Amazon: "All data analyzed by Amazon Personalize is kept private and secure, and only used for your customized recommendations."

Anyone know if there's something in the AWS contracts about Amazon using your data?


The AI services have a clause allowing them to use your data for training their models unless you opt out.


So I work for a distributor, and we've been discussing improving our up and cross sell abilities, due to the nature of our customer and product many of the products we distribute are deliberately not sold on Amazon, by and large I feel my company could use this without fear.


Are the products immune to being cloned and white-labelled by Amazon?


So our highest revenue product Amazon interestingly enough is already our biggest customer on, but the vendor refuses to go direct with Amazon and prefer we are the middle man. We have huge exposure here, but Amazon already has their recommenr dialed on this product so I wouldn't really care about them having a marginal amount of more info on it.

The rest of the product is largely under MAP policies excluding Amazon, and the products take a level of care and branding a bit more than Amazon's typical white label products. So, could they, yes, but we have plenty of bigger operational risks


My recommendations was not so bad: after CPU purchase Amazon suggest me RAM, HDD or another PC stuff - not overpriced and not cheap Chinese noname brands.


Long ago, when I was at Amazon, we used Collaborative Filtering pretty heavily. It's great when there's a heavily sweetened path with lots of things ordered together or in a chain.

It's horrible for moderately popular items though.


i'd find it more compelling if they'd base it on the tech they used circa 2004-2008. it's been years since their recommendations were valuable.


Any idea why this page has a tracking pixel from Linkedin?


Hidden amazon recruiting (tracking) effort maybe?


I'm curious to know how their algorithm works when a site offers truly unique items? In other words, if I was buying a custom hand-crafted ring what would it recommend?

Going a step further, if I was buying the Mona Lisa, would it know to recommend me other Da Vinci items, or other renaissance items, or would it fail to make that inference due to there being no co-occurrences in purchasing behaviour.


Heh. So Amazon gets to mine your product browsing event stream and conversion funnel, and use the insights to guide their own purchasing and manufacturing, and on top of that they'll charge you for giving them all your data. Brilliant!


This seems relevant for online retailers, although using a competing giants technology doesn't sound like it would be in their interest?


It is amazing how Amazon search & recommendation works almost never for me. Not sure about other people.


I remember having to build one with solr, that was a mess.


Wait, someone would use this? :P


If one uses this product do users have to be notified of this, in light of privacy / GDPR and all?

I built a very basic recommendation engine myself but would love to try out something with actual brains behind it, just being curious about the results.


Unlikely because you aren't going to share personal, identifiable information with this service; you would just send the primary key of the user in your database (usually an integer, guid, etc, as long as you aren't using some string that includes personal information like email or possibly username where they user picks their own name).

You can share all the information you want with other entities under GDPR as long as the information you are sending as long as you can't uniquely identify someone from that data _without_ additional information.




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