[DannyBee]

Proof? Most of these algorithms build models that do affinity weighting, and then try to guess at how the same some other person is as me (which is hard across such a variety of attributes).

There is no magic, so please explain the math/algorithms you think would work here.

(Sorry, it's just a lot of people wave the magic "algorithm" flag when faced with hard problems)

[gradys]

Well, Facebook can tell how often you interact with certain people. They can determine certain things about the content of those interactions. They can detect how similar your friend graph is to another person's. They know that your grandma is your grandma (if you have told them, of course), and they know, for example, that most people don't want to hang out with their grandma.

I haven't given you pseudocode for an actual algorithm, but I can imagine that Facebook could combine all of these metrics into an index that can tell them roughly how likely you are to want to hang out with certain people.

[angryasian]

without you telling explicitly who you want food recommendations from or tech product recommendations from its an incredibly difficult problem. Just because you talk to someone everyday doesn't mean you want their opinion on food or tv shows.

[tejaswiy]

Their "Close friends" algorithm does a pretty good job already. And even if it doesn't I'm sure most people have already removed / added friends as appropriate from that circle.

Facebook also has a bunch of data on how often you've interacted with various friends there .. so that could be one data point. (How often you're tagged in a check-in / photo, how often you've liked a post made by someone else etc.)

Your Your mom / dad / school friends generally may not be a good data point, while your college friends might be.

It'll take a lot of beta testing and tweaking, but I think it can be done.