I thought graph databases were the canonical implemention for recommendation systems, one of the few use cases I'd not go straight to sql
You don't need ML/AI, you need SQL
81–87 of 87 posts
Re: You don't need ML/AI, you need SQL
#82Tell you one of my favorite papers from NIPS this year
https://arxiv.org/abs/1712.01208
Suggests we would use ML for indexes. Not so much replace SQL but the thing the SQL runs against ;).
Re: You don't need ML/AI, you need SQL
#83The title is often true, but it doesn't mean too much or anything. And the same argument is brought up again and again in the past as well. What OP suggests, the so called SQL, is basically a heuristic based system. When done probably and carefully, it could of course work very well, and is indeed often used as baseline model to bootstrap a ML system. However, eventually the rule-based system will hit the wall, and M…
It's an odd one for me, because he sounds like he's trying to argue against the "when all you have is a hammer" approach to ML - but then goes on to describe SQL as his hammer.
For things like figuring out who your biggest customers are, SQL probably is the right tool for the job. Whale-spotting probably gives a decent bang per buck, and isn't particularly complex.
But when he gets onto recommendations, it starts to look like it's the author who's attached to the wrong tool for the job. His example of recommending sunglasses to people who buy sunglasses is terribly blunt. If someone in my locale, who doesn't regularly buy sunglasses, buys sunglasses; they're probably going on vacation - there's not much sun at home for them. Surely there's a whole raft of things someone excited for their summer holidays would impulse-buy, but the sunglasses they just bought are no longer on the list.
If ML can match them up with with a "going on summer holidays" demographic, and BI wants to sell them the only thing we know they no longer need, it's no longer making a strong case for blunt instruments.
Re: You don't need ML/AI, you need SQL
#84Earlier quoted context omitted.
Obviously ML can add a lot of value here, but its questionable to me if its trivial to build such a model with available data, keep said model up to date, or train variations on it easily, cheaply and quickly enough to A/B test the result and ensure you’re actually making any tangible difference. So you know... I don’t think it’s unfair to say that for smaller vendors, the cost/effort of setting up a ML model may dwa…
It's not unheard of to see +10-30% in revenue when adding a recommender system [0]; The system described by the author is arguably more complex than a recommender system, since he has to develop, maintain and evaluate a set of rules that are not based on real data, but only on his intuition of what users want. GP gave good examples of how this would easily fail (do you always want to recommend items from the same cat…
For newer or smaller firms the SQL approach makes sense. Both because there is less data, and it's less risky to implement. Once the context is fully fleshed out, then it's easier to move on to a ML recommender system. It's also easier to track improvement vs a benchmark.
Re: You don't need ML/AI, you need SQL
#85> say a person bought a pair of shoe, sunglasses and a book. For their newsletter, we will show include shoes, sunglasses and books. This was a lot more relevant than sending random stuff. I agree with the general sentiment of the article, but this seems like a poor example, since a more sophisticated approach can add a lot of value to a recommendation system. How do you know whether a customer is likely to want more…
> but this seems like a poor example In fact this is a perfect example of how NOT to do purchase-history-based suggestions, which unfortunately also seems to be how most companies do it. They see a big purchase (or search terms relating to one) and spam you with options for that purchase. But if I just bought a car, or a drone, or a laptop, then the last thing I want to see is ads for other cars or drones or laptops.…
I used to see that a lot on Amazon. I just bought an electric toothbrush, why would I buy another? Haven't been shopping much lately so not sure if it's gotten any better. My example is from last year, and I remember mentioning this problem in an interview with Amazon 6 years ago, and I'm sure machine learning has been involved for longer than that. It's still easier to screw up a machine learning model than SQL.
Re: You don't need ML/AI, you need SQL
#86> say a person bought a pair of shoe, sunglasses and a book. For their newsletter, we will show include shoes, sunglasses and books. This was a lot more relevant than sending random stuff. I agree with the general sentiment of the article, but this seems like a poor example, since a more sophisticated approach can add a lot of value to a recommendation system. How do you know whether a customer is likely to want more…
> but this seems like a poor example In fact this is a perfect example of how NOT to do purchase-history-based suggestions, which unfortunately also seems to be how most companies do it. They see a big purchase (or search terms relating to one) and spam you with options for that purchase. But if I just bought a car, or a drone, or a laptop, then the last thing I want to see is ads for other cars or drones or laptops.…
How about if you can identify a smart phone in proximity to a store display, and later target ads at that user? That would get you a lot of people that didn't buy a product, but demonstrated interest in a category. I suspect someone has already figured out how to make this happen, but I haven't found explicit confirmation.
Re: You don't need ML/AI, you need SQL
#87The real problem is people (including the author of the article, apparently) think ML is necessarily some kind of ultra-complicated technique that needs a PhD and a GPU. But, come on, 80% of the times you can use ML, dead-easy techniques are more than enough.
I mean, the author is talking about how SQL is a good-old 40 year old tech. In the mean time, one of the simplest ML algorithm, linear regression, is about 200 years old, even older (AFAIK) than Ada's program for Babbage's machine. It's very easy to understand and implement, and even excel has it as a standard function.
Sure, linear/logistic regression or naive bayes won't help you tag pictures with text à la facebook "this is a picture of a young man dancing with a red shirt", but the vast majority of use cases of ML are way easier, anyway. So yes, most of the time, you can easily find "talents" that will solve your ML problems. And if you really want to, you can implement it in SQL.