Hunch.com — A recommendation in 10 questions (or less)

From the Hunch "About" page:

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What should I be for Halloween? Do I need a Porsche? Should I dump that loser? Is Phoenix a good place to retire? Whom should I vote for? What toe ring should I buy?

It's a cruel world out there. Coin-flipping, I Ching consultation, closing your eyes and jumping, postponing the inevitable, Rock-Paper-Scissors, and asking your sister are all time-honored means of getting a recommendation — and yet we think there's room for one more: Hunch.

In 10 questions or less, Hunch will offer you a great recommendation to address your choice, problem, or dilemma, on thousands of topics. Hunch's recommendations are based on the collective knowledge of the entire Hunch community, narrowed down to people like you, or just enough like you that you might be mistaken for each other in a dark room. Hunch is designed so that every time it's used, it learns something new. That means Hunch's hunches are always getting better.

Hunch was started by clever folks who were exploring how machine learning could be used to guide practical, smart, and highly-customized recommendations.

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How it works here.

On a related note, knowing just how much you can trust what someone or some source has to say is an interesting exercise in assessing credibility.

One rule of thumb I've used since I was in my early teens, and that has borne up under decades of subsequent experience, is to try and find what the source has to say about something you're very knowledgeable about.

As a rule, at some point the reference will be inaccurate or just plain wrong.

By noting how far into the material the erroneous information appeared, you can pretty much judge just how much you can trust anything else generated by the same person or group.

It's quick and dirty and works well enough for most purposes.