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Are there examples of businesses out there which have done product recommendation correctly? Amazon is notorious for trying to sell you the same kind of produc

by gfo 8y ago

Are there examples of businesses out there which have done product recommendation correctly? Amazon is notorious for trying to sell you the same kind of product after you've already purchased it (e.g. I bought a ladder and now all of my recommendations are for more ladders). How hard can it be to uncover which items are more likely to be one-time buys and recommend the products people buy with those items instead (ex. show me painting supplies when I buy a ladder)?

virtuabhi 8y ago
Because there is x% chance that you might return this ladder and get another one. The ladder ads should go away after the expiration of free return period.
rhizome 8y ago
Are there examples of businesses out there which have done product recommendation correctly? I'm not sure what you mean by "correctly," but in general, if you're talking about automation and recommendation engines, my answer is "no." Of course, I don't know the goals of the companies implementing them and whether my sense of "correct" recommendations matches theirs. I don't think any of us do.
- gfo 8y ago
  Fair point. Correct is likely defined differently for the consumer and the business. Perhaps the answer is simple: Amazon is somehow making money from the example I cited - more money than showing the consumer relevant extras. Or they've done research to show there isn't much opportunity for more income by improving the algorithm for this.
s-shellfish 8y ago
I really feel like this just happens because they don't have enough people looking over the data recommendations and the actual market economics, or enough communication between both divisions, if those divisions are sufficiently black boxed from one another. The difference may be purchasing one ladder versus purchasing a ladder, returning a ladder, and purchasing another ladder. Or companies, contractors, etc. purchasing in bulk. Obviously if their core recommendation algorithms are sufficiently generalized to all item types, it doesn't matter that it's a ladder. You could be getting side effects from people buying pencils for classrooms in bulk or survivalists stocking up on canned goods. If the recommendation algorithms account for some categorical relation between item types, it still doesn't remove the problem, because different people in the world use different products differently and they can't all be classified and generalized to a collection of people's rule systems. It doesn't even begin to factor in the sorts of 'second order' logic required when new economic systems begin to grow on top of the existing supply-demand relation. You could be getting side effects from competitive buyers on amazon treating amazon products like they are stocks. It is hard. Dynamical systems.
- dhimes 8y ago
  I still have a hard time understanding how, if I have (to use your example) bought pencils in bulk, it makes sense to send me an ad for pencils in bulk? Especially, the exact same type/model/brand that I just bought. Does Amazon think I'll forget that I bought them from Amazon? Do they think I'll buy more before I have a need to? My hunch is that they aren't trying to improve those algorithms very hard. But it would be fascinating if there is something about human nature that I am simply not seeing here.
  - s-shellfish 8y ago
    It's not your information being connected to you directly. First your information goes into a system. That system is used to reason about how you buy things, and how someone similar to you buys things. This happens for every individual in the system. This eventually yields a cohesive model of behavior that can be used for probabilistic inferencing which effectively is establishing real time dynamic 'types' of both 'items being sold' and 'consumers making purchases'. Because it's so heavily generalized to a probabilistic/stochastic model, things that shouldn't get connected can get connected. You know the age old adage, correlation is not causation? That's exactly the problem with a model that is based entirely on relational linking of items with little to no oversight in ways to explain how it's actually functioning. I want to use the word self-referential because that's very much what these systems seem to be doing, it's like it literally loops in making one too many inferences about it's predictive space. I'm not saying this is precisely why it returns your choices back to you. But given that there's been some research (that I honestly haven't kept up with very much) that comes down to rigorously defining a data 'context' (for something like an RNN) and then rigorously searching the space for all data of what is a 'perfect match' for anything, all of this hyper perfect matching is bound to return you back to yourself in funny, ridiculous ways. And yes, I realize the many layers of irony for those people out there who would like to point it out. I can try to explain it and I'm making all the same mistakes it does. Trying to reason about this stuff is NOT easy because it makes your brain feel broken. And I don't build RNNs or anything like that, I've done a very small amount of research on recommendation engines when it was early days, I've taken engineering classes that study probability and stochastic processes which are used to model systems where the 'types' of variables are not always easily distinguishable from one another, but they must be organized and ordered into a system that can be reasoned about rigorously. It's like building circuits for people's minds and behaviors when they in their weakest decision making spaces (for lots of people). It's sometimes screwed up but you know, you know what you like, right haha, lol. Just blame Bezos he's the one with all the money https://en.wikipedia.org/wiki/Simpson%27s_paradox https://en.wikipedia.org/wiki/Simpson%27s_paradox
mattbierner 8y ago
Slightly different but spotify’s algorithmic recommendations have been great. I’ve found some really amazing stuff through “discover weekly”, “new releases”, and “related artists”. Same with Bandcamp. (Maybe the problem is easier for music?) For content, I also find that user curated lists can be far better than automated systems. They give me an upfront sense of the curator’s tastes and define niches that algorithms would never recognize
- rhizome 8y ago
  Are the categories you mention sorted by confidence? That is, is any part of what you like about them oriented around not having to scroll for the stuff you like the best?
  - mattbierner 8y ago
    "Discovery weekly" and "new releases" are not sorted as far as I know, but related artists may be. A key factor at play here may be that there is no "best", the variety of the recommendations feels like a positive for music whereas it could be a negative for a larger commitment like novel or for a consumer product. Even Spotify's objectively terrible recommendations (such as once recommending an album called "Music of Lorestan 1" for the synthwave artist "Saffari") turned out to be nice discoveries
- gfo 8y ago
  I agree with you here. Certainly Spotify has a different scenario - whether you pay or not, there's an extraordinary amount of content you can consume, and you do that with no added cost per consumption. Not to mention, they probably have several hundred times the amount of data to use for recommendations since a user changes song around every three minutes. Multiply that by the number of users and average number of songs someone listens to in a session... that's going to be a big number. Also, music choice is mostly preferential. Some preference is used when buying products but most of the time I feel like there's a need which influences the product purchase and this factors more heavily into the decision. Netflix I would say is a good counter-example of a content provider which is based more on preference but again, the content takes longer to consume so there's likely less data than Spotify. Not to mention, it seems they've been giving more recommendation preference to their originals lately.
klodolph 8y ago
Yes... CD Baby: https://sivers.org/hi https://sivers.org/hi
FireBeyond 8y ago
This, so very much. I bought a LaserJet printer. I went into my "Recommended for me" and the first four pages were probably 30% other printers, and 30% printer supplies (for my printers and others). How is this still a problem?
- shanghaiaway 8y ago
  Well, if the only actionable data they have about you is that you bought a printer from them, what else are they going to show you? This is the fallacy of machine learning: these systems aren't smart, they're incredibly dumb.
  - mmt 8y ago
    Except that's not the only datum they have. These aren't banner ads from a third party that's only getting a single keyword "printer". They also know which printer it was, when it was bought, and presumably they could know how long printers last, if not how long that specific printer lasts. The first is certainly enough not to recommend supplies for a different printer. > This is the fallacy of machine learning: these systems aren't smart, they're incredibly dumb. Here, I agree. They're generally no smarter than the people programming them.. reminiscent of any computer system.
  - FireBeyond 8y ago
    Given that I have upwards of 2,000 Amazon orders spanning ten years, not so much.

by gfo 8y ago

virtuabhi 8y ago

Because there is x% chance that you might return this ladder and get another one. The ladder ads should go away after the expiration of free return period.

rhizome 8y ago

Are there examples of businesses out there which have done product recommendation correctly? I'm not sure what you mean by "correctly," but in general, if you're talking about automation and recommendation engines, my answer is "no." Of course, I don't know the goals of the companies implementing them and whether my sense of "correct" recommendations matches theirs. I don't think any of us do.

gfo 8y ago
Fair point. Correct is likely defined differently for the consumer and the business. Perhaps the answer is simple: Amazon is somehow making money from the example I cited - more money than showing the consumer relevant extras. Or they've done research to show there isn't much opportunity for more income by improving the algorithm for this.

s-shellfish 8y ago

I really feel like this just happens because they don't have enough people looking over the data recommendations and the actual market economics, or enough communication between both divisions, if those divisions are sufficiently black boxed from one another. The difference may be purchasing one ladder versus purchasing a ladder, returning a ladder, and purchasing another ladder. Or companies, contractors, etc. purchasing in bulk. Obviously if their core recommendation algorithms are sufficiently generalized to all item types, it doesn't matter that it's a ladder. You could be getting side effects from people buying pencils for classrooms in bulk or survivalists stocking up on canned goods. If the recommendation algorithms account for some categorical relation between item types, it still doesn't remove the problem, because different people in the world use different products differently and they can't all be classified and generalized to a collection of people's rule systems. It doesn't even begin to factor in the sorts of 'second order' logic required when new economic systems begin to grow on top of the existing supply-demand relation. You could be getting side effects from competitive buyers on amazon treating amazon products like they are stocks. It is hard. Dynamical systems.

dhimes 8y ago
I still have a hard time understanding how, if I have (to use your example) bought pencils in bulk, it makes sense to send me an ad for pencils in bulk? Especially, the exact same type/model/brand that I just bought. Does Amazon think I'll forget that I bought them from Amazon? Do they think I'll buy more before I have a need to? My hunch is that they aren't trying to improve those algorithms very hard. But it would be fascinating if there is something about human nature that I am simply not seeing here.
- s-shellfish 8y ago
  It's not your information being connected to you directly. First your information goes into a system. That system is used to reason about how you buy things, and how someone similar to you buys things. This happens for every individual in the system. This eventually yields a cohesive model of behavior that can be used for probabilistic inferencing which effectively is establishing real time dynamic 'types' of both 'items being sold' and 'consumers making purchases'. Because it's so heavily generalized to a probabilistic/stochastic model, things that shouldn't get connected can get connected. You know the age old adage, correlation is not causation? That's exactly the problem with a model that is based entirely on relational linking of items with little to no oversight in ways to explain how it's actually functioning. I want to use the word self-referential because that's very much what these systems seem to be doing, it's like it literally loops in making one too many inferences about it's predictive space. I'm not saying this is precisely why it returns your choices back to you. But given that there's been some research (that I honestly haven't kept up with very much) that comes down to rigorously defining a data 'context' (for something like an RNN) and then rigorously searching the space for all data of what is a 'perfect match' for anything, all of this hyper perfect matching is bound to return you back to yourself in funny, ridiculous ways. And yes, I realize the many layers of irony for those people out there who would like to point it out. I can try to explain it and I'm making all the same mistakes it does. Trying to reason about this stuff is NOT easy because it makes your brain feel broken. And I don't build RNNs or anything like that, I've done a very small amount of research on recommendation engines when it was early days, I've taken engineering classes that study probability and stochastic processes which are used to model systems where the 'types' of variables are not always easily distinguishable from one another, but they must be organized and ordered into a system that can be reasoned about rigorously. It's like building circuits for people's minds and behaviors when they in their weakest decision making spaces (for lots of people). It's sometimes screwed up but you know, you know what you like, right haha, lol. Just blame Bezos he's the one with all the money https://en.wikipedia.org/wiki/Simpson%27s_paradox https://en.wikipedia.org/wiki/Simpson%27s_paradox

mattbierner 8y ago

Slightly different but spotify’s algorithmic recommendations have been great. I’ve found some really amazing stuff through “discover weekly”, “new releases”, and “related artists”. Same with Bandcamp. (Maybe the problem is easier for music?) For content, I also find that user curated lists can be far better than automated systems. They give me an upfront sense of the curator’s tastes and define niches that algorithms would never recognize

rhizome 8y ago
Are the categories you mention sorted by confidence? That is, is any part of what you like about them oriented around not having to scroll for the stuff you like the best?
- mattbierner 8y ago
  "Discovery weekly" and "new releases" are not sorted as far as I know, but related artists may be. A key factor at play here may be that there is no "best", the variety of the recommendations feels like a positive for music whereas it could be a negative for a larger commitment like novel or for a consumer product. Even Spotify's objectively terrible recommendations (such as once recommending an album called "Music of Lorestan 1" for the synthwave artist "Saffari") turned out to be nice discoveries
gfo 8y ago
I agree with you here. Certainly Spotify has a different scenario - whether you pay or not, there's an extraordinary amount of content you can consume, and you do that with no added cost per consumption. Not to mention, they probably have several hundred times the amount of data to use for recommendations since a user changes song around every three minutes. Multiply that by the number of users and average number of songs someone listens to in a session... that's going to be a big number. Also, music choice is mostly preferential. Some preference is used when buying products but most of the time I feel like there's a need which influences the product purchase and this factors more heavily into the decision. Netflix I would say is a good counter-example of a content provider which is based more on preference but again, the content takes longer to consume so there's likely less data than Spotify. Not to mention, it seems they've been giving more recommendation preference to their originals lately.

klodolph 8y ago

Yes... CD Baby: https://sivers.org/hi https://sivers.org/hi

FireBeyond 8y ago

This, so very much. I bought a LaserJet printer. I went into my "Recommended for me" and the first four pages were probably 30% other printers, and 30% printer supplies (for my printers and others). How is this still a problem?

shanghaiaway 8y ago
Well, if the only actionable data they have about you is that you bought a printer from them, what else are they going to show you? This is the fallacy of machine learning: these systems aren't smart, they're incredibly dumb.
- mmt 8y ago
  Except that's not the only datum they have. These aren't banner ads from a third party that's only getting a single keyword "printer". They also know which printer it was, when it was bought, and presumably they could know how long printers last, if not how long that specific printer lasts. The first is certainly enough not to recommend supplies for a different printer. > This is the fallacy of machine learning: these systems aren't smart, they're incredibly dumb. Here, I agree. They're generally no smarter than the people programming them.. reminiscent of any computer system.
- FireBeyond 8y ago
  Given that I have upwards of 2,000 Amazon orders spanning ten years, not so much.