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• Hello, my name is Christian Rudder,

• and I was one of the founders of OK Cupid.

• It's now one of the biggest dating sites in the United States.

• Like almost everyone at the site,

• I was a math major, and, as you might expect,

• we're known for the analytic approach

• we have taken to love.

• We call it our matching algorithm.

• Basically OK Cupid's matching algorithm helps us decide whether two people should go on a date.

• We built our entire business around it.

• Now, algorithm is a fancy word,

• and people like to drop it like it's this big thing,

• but, really, an algorithm is just a systematic,

• step-by-step way to solve a problem.

• It doesn't have to be fancy at all.

• Here, in this lesson, I'm going to explain

• how we arrived at our particular algorithm

• so you can see how it's done.

• Now, why are algorithms even important?

• Why does this lesson even exist?

• Well, notice one very significant phrase I used above:

• they are a "step-by-step" way to solve a problem,

• and, as you probably know,

• computers excel at step-by-step processes.

• A computer without an algorithm

• is basically an expensive paperweight.

• And since computers are such a pervasive part of everyday life, algorithms are everywhere.

• The math behind OK Cupid's matching algorithm

• is surprisingly simple.

• multiplication,

• a little bit of square roots.

• The tricky part in designing it, though,

• was figuring out how to take something mysterious,

• human attraction,

• and break it into components that a computer can work with.

• Well, the first thing we needed to match people up was data,

• something for the algorithm to work with.

• The best way to get data quickly from people

• is to just ask for it.

• So, we decided that OK Cupid should ask users questions,

• stuff like, "Do you want to have kids one day?"

• and "How often do you brush your teeth?",

• "Do you like scary movies?"

• and big stuff like "Do you believe in God?"

• Now, a lot of the questions are good

• for matching like with like,

• that is when both people answer the same way.

• For example, two people who are both into scary movies are probably a better match than one person who is and one person who isn't.

• But what about a question like,

• "Do you like to be the center of attention?"

• If both people in a relationship are saying yes to this,

• then they are going to have massive problems.

• We realized this early on,

• and so we decided we needed

• a bit more data from each question.

• but the answer they wanted from someone else.

• That worked really well,

• but we needed one more dimension.

• Some questions tell you more about a person than others.

• For example, a question about politics, something like,

• "Which is worse: book burning or flag burning?"

• might reveal more about someone than their taste in movies.

• And it doesn't make sense to weigh all things equally,

• so we added one final data point.

• For everything that OK Cupid asks you,

• you have a chance to tell us

• the role it plays in your life,

• and this ranges from irrelevant to mandatory.

• So now, for every question,

• we have three things for our algorithm:

• second, how you want someone else,

• and three, how important the question is to you at all.

• With all this information,

• OK Cupid can figure out how well two people will get along.

• The algorithm crunches the numbers and gives us a result.

• As a practical example,

• let's look at how we'd match you with another person,

• let's call him, "B".

• Your match percentage with B is based on

• Let's call that set of common questions, "s".

• As a very simple example, we use a small set "s"

• with just two questions in common

• and compute a match from that.

• Here are our two example questions.

• The first one, let's say, is, "How messy are you?"

• and the answer possibilities are

• very messy,

• average,

• and very organized.

• And let's say you answered "very organized,"

• and you'd like someone else to answer "very organized,"

• and the question is very important to you.

• Basically you are a neat freak.

• You're neat,

• you want someone else to be neat,

• and that's it.

• And let's say B is a little bit different.

• He answered very organized for himself,

• but average is OK with him

• as an answer from someone else,

• and the question is only a little important to him.

• Let's look at the second question,

• it's the one from our previous example:

• "Do you like to be the center of attention?"

• The answers are just yes and no.

• how you want someone else to answer is "no,"

• and the questions is only a little important to you.

• Now B, he's answered "yes,"

• he wants someone else to answer "no,"

• because he wants the spotlight on him,

• and the question is somewhat important to him.

• So, let's try to compute all of this.

• Our first step is,

• since we use computers to do this,

• we need to assign numerical values

• to ideas like "somewhat important" and "very important"

• because computers need everything in numbers.

• We at OK Cupid decided on the following scale:

• irrelevant is worth 0,

• a little important is worth 1,

• somewhat important is worth 10,

• very important is 50,

• and absolutely mandatory is 250.

• Next, the algorithm makes two simple calculations.

• The first is how much did B's answers satisfy you,

• that is, how many possible points did B score on your scale?

• Well, you indicated that B's answer

• to the first question about messiness

• was very important to you.

• It's worth 50 points and B got that right.

• The second question is worth only 1

• because you said it was only a little important,

• and B got that wrong.

• So B's answers were 50 out of 51 possible points.

• That's 98% satisfactory.

• It's pretty good.

• And, the second question of the algorithm looks at

• is how much did you satisfy B.

• to the messiness question

• Of those, 11, that's 1 plus 10,

• you earned 10,

• you guys satisfied each other on the second question.

• equals 91% satisfactory to B.

• The final step is to take these two match percentages

• and get one number for the both of you.

• To do this, the algorithm multiplies your scores,

• then takes the nth root,

• where n is the number of questions.

• Because s, which is the number of questions,

• in this sample, is only 2,

• we have match percentage equals

• the square root of 98% times 91%.

• That equals 94%.

• That 94% is your match percentage with B.

• It's a mathematical expression

• of how happy you'd be with each other

• based on what we know.

• Now, why does the algorithm multiply as opposed to, say,

• average the two match scores together

• and do the square-root business?

• In general, this formula is called the geometric mean,

• which is a great way to combine values

• that have wide ranges

• and represent very different properties.

• In other words, it's perfect for romantic matching.

• You've got wide ranges

• and you've got tons of different data points,

• like I said, about movies,

• Intuitively, too, this makes sense.

• Two people satisfying each other 50%

• should be a better match

• than two others who satisfy 0 and 100,

• because affection needs to be mutual.

• After adding a little correction for margin of error,

• in the case when we have a very small number of questions,

• like we do in this example,

• we're good to go.

• Any time OK Cupid matches two people,

• it goes through the steps we just outlined.

• then it compares your choices and preferences

• to other people in simple, mathematical ways.

• This, the ability to take real world phenomena and make them something a microchip can understand, is, I think, the most important skill anyone can have these days.

• Like you use sentences to tell a story to a person,

• you use algorithms to tell a story to a computer.

• If you learn the language,

• you can go out and tell your stories.

Hello, my name is Christian Rudder,

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A2 US TED-Ed algorithm cupid answer question matching

【TED-Ed】Inside OKCupid: The math of online dating - Christian Rudder

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Why Why posted on 2019/01/10
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