But that's not how Facebook sells ads. It would be one thing to recommend more content like what you've engaged with already. It's another thing to let advertisers check boxes that let them say "sell my product to gay men between the ages of 25 and 35" and to satisfy that without ever having the user tell you their orientation or age. Facebook deliberately crunches the numbers and produces a clear signal from them that advertisers can filter on.
Creating a lookalike audience is literally just saying "create ad targeting that parallels the things that would target this other group of people". Just because you're not specifically choosing interests or behaviors and instead relying on automation to do the work doesn't mean that's not what's happening under the hood. "Show this ad to mothers" is effectively the same as "here's 100 mothers, show this ad to people like them".
This differs from betting shown recommendations because you're actively being targeted.
Hmm, not exactly. It’s more akin to “here’s 100 customers, show my ad to others who have similar interests/behaviors”. You don’t upload a list of 100 identical customers, nor would that be useful for lookalikes.
And to my point, in no way is this equivalent to "you liked this one thing, so here's another thing you might like". It's still targeting. Facebook isn't arbitrarily showing you ads. "People like this" is what advertisers set, whether that comes from the advertiser checking boxes or providing a sample of similar users is an unimportant distinction.
At no point is Facebook ever making the self directed decision to show an ad to someone that doesn't positively match some criteria that the advertiser specified. That would be wild, and as I said, that's not how Facebook sells ads.
No, it's not selected criteria. Your POV seems focused on an easily explainable classification style model. However, Ads (or any recommender) ML models have been using black box neural networks for a long while now. Lookalike audiences are most commonly used when the Advertiser does not know the criteria - instead hoping FB/Google can magically match their ads with users who are "similar" in web activity or whatever, and hopefully lead to conversions.
It's hard to explain a black box, but for eg, a Todo list SaaS company uploads a list of customers most likely having a diverse set of interests and behaviors. The NN matches more users that all seem like they'd be likely to buy such a SaaS because it figured they're in the market, or have disposable income, or maybe have searched for productivity solutions, or maybe have browsed some competitors, or have the same apriori but unrelated actions as someone who ended up doing the above or a combination of all of these.
> The NN matches more users that all seem like they'd be likely to buy such a SaaS because it figured they're in the market, or have disposable income, or maybe have searched for productivity solutions, or maybe have browsed some competitors
If the audience you provide has none of these attributes, there's no way for it to magically intuit that you want these things from your customers. The neutral net doesn't know what a to-do list is or who uses one. It doesn't know the price of your service. The only thing happening is it's determining which of the signals about the users in your source audience are the most impactful. By supplying the source audience, you're supplying criteria for who your ads should be shown to.
Whether you're explicitly setting those things or not, you're still supplying criteria. You can't swap this system for a recommendation system, they don't work the same way at all.
