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Comment by ekarulf | Hacker News Reader

Amazon Personalization.

We use ML/Deep Learning for customer to product recommendations and product to product recommendations. For years we used only algorithms based on basic statistics but we've found places where the machine learned models out perform the simpler models.

Here is our blog post and related GitHub repo: https://aws.amazon.com/blogs/big-data/generating-recommendat... https://github.com/amznlabs/amazon-dsstne

If you are interested in this space, we're always hiring. Shoot me an email ($my_hn_username@amazon.com) or visit https://www.amazon.jobs/en/teams/personalization-and-recomme...

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So is this like the Amazon "feature" where I buy a coffee table on Amazon, then I get suggested to buy a coffee table EVERY DAY for 3 months. Literally row after row of coffee table? Because there must be a big pool of people who buy 1 coffee table buying more coffee tables immediately after?

It's a hard problem to determine the repeat purchase cadence of a product. At one end of the bell curve you have items re-purchased frequently, e.g. diapers or grocery, and on the other end you have items that are rarely repurchased.

I haven't looked at coffee tables specifically, but I know when I've looked at home products in the past I've been surprised at how frequently people will buy two large items, e.g. TVs or furniture, within a short period. That said, I agree there is room for improvement here. We're constantly running experiments to improve the customer experience, I have faith that in the limit things will improve. Again, we have no shortage of experimental power so if you'd like to join in the experimentation let me know :)

IMO it comes down to the fact that Amazon literally has my last 13 years of purchasing history, yet it seems that all they are doing is "you looked at x, lets show you y variations of that x."

My dream is that I go to Amazon.com and there are a ton of different unrelated products that people who purchase similar things as me buy. So if I only buy "buy it for life" kitchen equipment, it doesn't show me the most popular but crappy version of something, it shows me the one that I'd actually purchase.

Such an easy problem with suuuuuch a difficult solution though. Not to mention the obvious privacy concerns there.

Oh well, I know that they have good people working on the solution, and no chance I could do it better :p

This answer doesn't quite satisfy :-)

This topic must be extremely interesting (good suggestions could increase sales by a LOT) and smart people must have been working on it for quite a while.

- What is the fundamental reason why this is a hard problem?

- What's up with the coffee tables specifically, could you, for the hell of it, look into that category and tell us what the actual related products are? Let us (fail to) guess how these products are related, but don't let us hanging :-)

"It's a hard problem to determine the repeat purchase cadence of a product."

I don't think it is.

Do you work at Amazon, or do you have experience in this area? Care to elaborate?

Do you have any obfuscated training sets available to public?

edit:typo

must be the same genius technology that leads Amazon to load up my Prime frontpage with fashion accessories when I've never had any history of searching or buying such, and recommending the same shows "Mozart in the Jungle", "Transparent", "Catastrophe" on Fire TV stick for months even though I've never shown any interest in any of such programming, even after manually "improving recommendations" by clicking "Not Interested".

its amazing that the vaunted Amazon technology is unable to figure out an algorithm that would satisfy a user's deep desire "please stop plastering Jeffrey Tambor's lipstick and mascara covered face on my startup screen, I've gotten tired of looking at it for the past year"

perhaps you should change your Amazon password...

Your purchase was merely the inaugural move to establish your newfound hobby of coffee table collecting.

I read this same tweet last week too :)

Advertising is trained against ROI, not against what will "seem right" to the user.

Maybe in-market* furniture shoppers tend to spend a lot of money. Maybe furniture is a very profitable category. Even if the system is smart enough to assume there's only a 20% chance that you're in the process of significant furniture purchases, furniture ads may still be a better use of the ad slot than a lower value item where you have an 80% chance of being in-market.

Then why show the same damn coffee table over and over? Maybe that's more likely to return your attention to your furniture purchasing? I have no idea. Most likely, they don't know exactly either. Most likely, that's just what the highest-scoring current algorithm decided.

*The duration of "in-market" varies by category. Some product categories have a long consideration phase. For example car shoppers tend to spend 2-3 months considering alternative brands and models before they spend a few weeks narrowing down on a specific car configuration and exact pricing.

Haha yes, I remember seeing washing machines on my landing page for months after I bought one from Amazon. I mean, how many of them could a person need?

Seriously though, I don't understand why it's so hard to take this effect into account, as there should be a very strong negative correlation between a purchase in a given category and the probability of buying an article from that category in the near future, so even a simple ML algorithm should be able to pick this up easily. Anyone here who can explain why this is difficult?

The simple algorithm is to build a correlation matrix between of purchases between all items in the store. Then, when given an item to generate recommendations for, you provide the other items with the highest scores, with a "top sellers" correction for the items that are correlated with everything.

I used to work for a company that implemented similar recommendation services. We approached this problem by modelling whether or not a category was likely to have recurring purchases.

A pleasing explanation is that it is a book store.

(I'm not saying it is a good or likely explanation)

Obviously the goal of ML in this would be that feeding it enough data about users who buy coffee tables would eventually teach it that you probably don't want another coffee table (because who buys two coffee tables in a row?), but might want to buy say... end tables or other living room furniture in a matching style to the coffee table you just bought.

Not saying it works, but that'd be the goal.

Disclaimer: I know nothing about ML.

Would the standard models used allow for the fact that humans could, after buying a coffee table, choose to click on the coffee table in anticipation of then getting suggestions for similar furniture. Presumably the machine sees that the end goal of those continually clicking the same item is actually to arrive at similar items .. but wouldn't it be an obvious optimisation for Amazon to set the ML up to already look deeper than the first page reached.

I have a similar thing with Amazon, I don't know how you're supposed to access the bestseller list for a product type. I just know that if you search a product and follow related products that you eventually get a "#5 in ObscureProduct" tag and that tag takes you to the list of the top-10 models of ObscureProduct available. That sort of learnt navigation must play havoc with a suggestion algo (but IMO would be very easy to fix with just a link for any specific enough item to the 'top 10 in this category').

Predictive analytics tells them you need more coffee tables. Many many more.

Theory is that the recommendation engine is built for books. So if you buy a specific type of book, it recommends other books in the similar category. I guess they never got a chance to update it to reflect the fact that Amazon sells more than just books.

And if you buy a lot of coffee table books, you need more coffee tables.

I'm late - but that is actually called dynamic remarketing. You look at a certain category of item and then see ads (on amazon or off-site) for other items in that same category. If you actually bought the coffee table on a different device/browser/anywhere else.. then you'll see those ads for a while because they can't recognize that you actually made the purchase already.

I get similar for travel guide books which are the kind of think you buy once when visiting a place you have never visited before.

I get suggestions for travel guide books for the same country I visited a year ago for which I purchased a guide book.

It's more like you bought a coffee table and you get coffee beans in the recommendations. Also, you buddy who you are in the same group with gets a coffee table recommendation.

I guess this would make more sense.

    For years we used only algorithms based on basic statistics but we've found places where the machine learned models out perform the simpler models.

This is the right way to approach it. Too many people are looking for "deep" as some sort of silver bullet for an ill defined problem they have. If you can't validate against a simple model trained properly you are already in trouble. Likewise if you don't understand how to evaluate your generalization issues and how/if a simpler model will improve them.

Frankly, Amazon recommendations suck, they suck really hard.

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