But there are so many other things that make more sense: just imagine what a salesman would try to do, and he answer is not "another vacuum". They could try to get me to buy bags to go with the vacuum. They might think I am into cleaning right now and try to sell me a feather duster. It just makes so little sense that their reaction to "bought a thing" is to run ads showing me "same thing you just bought" instead of "things that go well with the thing you just bought"... particularly as they know what those things are as before you leave Amazon's website they are all over "people who bought this tend to buy this also". When I buy a camera they should try to sell me a lens cap. When I buy exercise equipment they should try to sell me weights or towels or mats. When I buy a DVD player they should try to sell me DVDs. Yet for the next two weeks they insist on spending as money showing me the thing I just bought as if I want to buy it again: what the hell? :/

Definitely! They should be using Apriori or some other association rule learning algorithm for this.

They already use these techniques to suggest things before you make the purchase, but it would be great to extend it over a long period of time, like you said. A few months later they suggest carpet cleaner or bags for your vacuum.

Heck, The small online shop where I bought my robot lawnmower does this. Last year I bought replacement knifes for the robot. This year they sent me a mail at the same time, asking if I wanted to buy another pair of replacement knifes, or perhaps other accessories or a new battery. And yes, I wanted another set of knifes - click, done.

Indeed, but that would require common sense. That's a lot of work, as Doug Lenat will readily confirm.

My instinct is that they are willing to live with showing some comparatively weak recommendations occasionally if that means not adding layers and layers of exceptions on top of their basic recommendation engine. And/or they have projected that the ROI isn't there for building in more sophisticated recommendations.

At Amazon's scale, a very very small increase in the effectiveness of recommendations can represent millions in revenue. They probably do have layers of exceptions, and custom-built software used by teams of "editors" to manually manage these exceptions. Not to mention the experimentation system where they are simultaneously testing hundreds of things from CSS one-liners to new ranking algorithms.

It's probably a pretty complex beast with the weight and inertia of a core system that's been continuously in production through 20 years of growth. They probably continue to invest heavily in it, the ROI is there, it's just a really hard problem for them at this point. Their engineers probably curse its limitations and would love to rewrite/replace old and outdated parts, and indeed there will be teams working on that, but those projects will either die out or spend so much time reaching feature parity with the bloated existing system that it ends up looking much the same.

Why would this be an "exception"? The basic item-based collaborative filtering described at the beginning of the article finds items that are likely to be purchased by someone who has purchased a specific item.

I'm pretty sure that it's true that someone who buys a vacuum is much more likely to buy another vacuum. It may seem counterintuitive, and may not be true for you after you just bought a vacuum, but I see no reason to believe that Amazon's algorithm isn't working properly and giving Amazon a lot of value.

It seems like we're all saying the same things - I agree it is likely that someone who has bought a vacuum before is more likely to buy another. But to make the recommendations really shine, it should wait n months before suggesting that again, where n will vary quite a lot by product type - those are the sort of exceptions I'm talking about.

It should not require layers and layers of exceptions to categorize products that get repeat buys (paper towels), similar buys (detective novels), or one-time buys (vacuum cleaners), and make recommendations accordingly.

> This is obviously speculation, but I think Amazon could come up with something better if they felt it pressing i.e. recommending the same product category was no better than random products.

You'd think they'd have so much data on buyer behaviour that they could recommend you e.g. other cleaning products that vacuum cleaner buyers tend to buy like mops, dusters and sprays. I can't imagine how recommending you the same thing you just bought over and over that you're not likely to get again for years is going to yield the best results and even manually selected recommendations would do better than that but willing to be convinced as obviously it's in their best interest to be smart here. You'd think you'd even explicitly build in a rule in your recommender system not to recommend something that has been recently bought.

I wonder if the issue is that essentially, nearly everyone buys a vacuum at some point. So, you end up where the only distinct group of vacuum-buyers from whom you can drive recommendation behaviour is people who use Amazon only to buy vacuums, basically you're clustered into either the entire world or a tiny group of Amazon vacuum fanatics.

Another issue might just be that the recommendation engine updates almost immediately, go to the homepage after taking any action (browsing, buying) and it's already updated. So they might've gone for "good enough, but fast" rather than "accurate, but slow"