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This could've been a valid criticism of people that use ML where it's not appropriate, but it ended up being a bit of an irrational rant, and a dishonest one to
by halflings 8y ago
This could've been a valid criticism of people that use ML where it's not appropriate, but it ended up being a bit of an irrational rant, and a dishonest one too:
> I mean, why send a letter with breast pumps to a man that just bought a pair of sneakers? It doesn't even make sense. Typical open rate for most marketing emails is anywhere between 7 - 10%. But when we do our work well, we saw close to 25 - 30%.
How do you know what items are compatible to each other? Why only recommend sneakers to somebody with sneakers, instead of also recommending sport clothing?
Oh, I guess you could build some type of topology of all your shopping items. But what about recommending soccer balls to people that bought soccer shoes? You could also add that to your database, but now you also need a heuristic to score item similarity: `category_matches * 10 + subcategory_matches * 5 + color_matches * 2 + ...`
This is the whole point of ML. People have been building rule-based systems built on "domain expertise" for ages, only to find that they are limited and cannot compete with simple algorithms fed with enough data.
- reacharavindh 8y agoBut, that might be in the realm of SQL too. Find out what items were frequently bought with the item that this customer bought, and send them as recommendations.. Rule-based does not always mean that a user is sitting down writing that tennis balls and tennis shoes are related items. Don't you think?
- visarga 8y agoExpert systems are brittle and don't generalise well outside the data on which were created. You know, counting items and dividing by total number is a kind of machine learned model, too. In technical terms it is "Computing the maximum likelihood estimate (MLE) for the PMF of a random variable taking finitely many values." But it's a poor man's model. That's why in order to solve complex problems we use stuff like neural nets and gradient boosting, and in unsupervised learning, matrix factorisation.
- ju-st 8y agoSuch a simple system would recommend many items that are frequently bought by everyone (like bread, toilet paper, batteries). You would have to weight the items in some fancy way to get useful recommendations... And I have just described the introducing slides of a applied machine learning university lecture.
- ashelmire 8y agoThis article and thread gives the impression that it’s dominated by people who don’t even have a rudimentary understanding of ML. If they think SQL is a replacement for ML, I’m really not sure what they’re doing in this field. ML is for making sense of data in a large number of ways beyond “hey let’s query a database for some trivial information”.
- reacharavindh 8y agoTrue. I don't think the OP or I deny that. The premise is that some practitioners use ML as an overkill for things that are simple enough to be solved by SQL.