Yeah, you'd expect more from ML at this point. I wonder how much of ML research actually gets utilized in industry.
Amazon is not an idiot:
> You need two insights here:
> 1) Conditional probability is a mathematical technology that does exist.
> 2) Buying X is not entirely random across the population.
> The ways X is not random vary based on the good [math]. Consider refrigerators.
> You probably buy one every ten years. If I don't know where you are in your refrigerator cycle, my prior estimate should be 2.7e-4 probability of you buying it tomorrow.
> Suppose I know you bought yesterday. In my life on earth, I have realized that buyer's remorse is a thing.
> What's a SWAG for how often a purchase immediately goes wrong? Not right color? Fridge DOA? Shoot I mismeasured my kitchen? Wife just hates it? Call that 2%. If I fix it within a week, then 2% / 7 = 2.9e-3 probability of purchasing a new fridge.
> That's a 10X relative risk.
Source: patio11 (Patrick McKenzie)
So many people complain about this behavior of recommender systems and here comes Patrick dropping some math and saying: “Well, actually there’s this and this probability for this to happen”.
Don’t piss on me and tell me that it’s raining. Before I make a purchase, especially online, I do my research and make a choice, that’s it.
I’ve never returned anything online and I’ve never needed to buy a second item after buying the first one.
If you insist that your math is right, give me a button I can press so I don’t have to care about your probabilities.
Maybe it is, but I'm not totally convinced. For one it doesn't explain why Amazon would recommend literally the same SKU so often. Also, does Amazon really want to incentivize returning large items like fridges?
And the poker thing from the original post
> People who are compensated strictly based on their ability to predict the future, like poker players... tend to be much better at high school math than Twitter users.
Professional gamblers in games like poker usually don't try to predict the future. They just have a small set of hands with known probability and mainly have to focus on things like sizing their bets. (Poker players also have to read human signals, but I'm not sure they explicitly assign probabilities to these).
Another explanation is just that the cost of recommending you a fridge you already own is lower than the cost of tuning the algorithm better or having multiple algorithms depending on tuples of factors (price, customer, customer behavior). If that's true, then we should expect Amazon to do less of this in the future. If Patio11's explanation is correct, then we should expect Amazon to continue to recommend these items even as the algorithms are updated to the more recent generation of AI.
Whether poker players are predicting the future or not, I would argue Amazon is doing the same thing:
- Each potential customer can be considered a hand with a certain probability.
- Amazon is sizing their "bet" (ad budget) according to the probability that customer will convert.
I also agree with this observation about poker, and can imagine Amazon using a similar strategy: https://hw.leftium.com/#/item/39462238
Successful marketers were already teaching this decades ago: https://thegaryhalbertletter.com/newsletters/direct_marketin...