TedTest

From the Ted Talk by Christian Rudder: Inside OKCupid The math of online dating

Unscramble the Blue Letters

Hello, my name is Christian Rudder, and I was one of the founders of OkCupid. It's now one of the biggest dinatg sites in the United States. Like most everyone at the site, I was a math major, As you may expect, we're known for the analytic approach we take to love. We call it our matching algorithm. Basically, OkCupid's mcnitahg 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 pberolm. 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 ahomrigtls even important? Why does this loessn even exist? Well, nctioe one very sciniafnigt phrase I used above: they are a step-by-step way to solve a problem, and as you probably know, crmtupoes eecxl at step-by-step processes. A computer without an algorithm is basically an expensive paperweight. And since computers are such a paresvvie part of everyday life, algorithms are everywhere. The math behind OkCupid's matching algorithm is surprisingly simple. It's just some aiitddon, multiplication, a little bit of square roots. The tricky part in dnegisnig it was figuring out how to take something mrsteiuoys, human attraction, and break it into components that a computer can work with. The first thing we needed to match popele 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 OkCupid should ask uesrs questions, stuff like, "Do you want to have kids one day?" "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 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, they're going to have massive problems. We realized this early on, and so we ddceeid we needed a bit more data from each question. We had to ask people to specify not only their own answer, 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 pseorn 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 okpicud 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: first, your answer; second, how you want someone else — your potential mtcah — to answer; and third, how important the question is to you at all. With all this information, OkCupid can figure out how well two people will get along. The alotghrim 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 baesd on questions you've both answered. Let's call that set of common qnuisetos "s." As a very simple example, we use a samll 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, aavgere and very oeanrgzid. And let's say you aernewsd "very organized," and you'd like someone else to anwser "very organized," and the question is very important to you. Basically, you're 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, from our previous example: "Do you like to be the center of attention?" The answers are "yes" and "no." You've answered "no," you want someone else to answer "no," and the question 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 iartnopmt to him. So, let's try to couptme all of this. Our first step is, since we use computers to do this, we need to assign numerical values to iedas like "somewhat important" and "very important," because computers need everything in numbers. We at OkCupid 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 socre on your scale? Well, you indicated that B's answer to the first question, about mesessnis, was very important to you. It's wtroh 50 points and B got that right. The second qtuoiesn is worth only 1, because you said it was only a little important. B got that wrong, so B's answers were 50 out of 51 possible points. That's 98% sfscaotatriy. Pretty good. The second question the algorithm looks at is: How much did you satisfy B? Well, B placed 1 point on your answer to the messiness question and 10 on your answer to the second. Of those 11, that's 1 plus 10, you earned 10 — you guys satisfied each other on the second question. So your answers were 10 out of 11 elauqs 91 precent satisfactory to B. That's not bad. The final step is to take these two match percentages and get one number for the both of you. To do this, the algorithm metllipuis 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 percent times 91 percent. That equals 94 percent. That 94 percent is your match percentage with B. It's a mathematical eerossixpn of how happy you'd be with each other, based on what we know. Now, why does the algorithm mlultpiy, 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. It's a great way to cbionme values that have wide ranges and represent very different properties. In other words, it's perfect for romantic matching. You've got wide ragens and you've got tons of different data points, like I said, about movies, politics, religion — everything. Intuitively, too, this makes sense. Two people satisfying each other 50 percent should be a better match than two others who stasify 0 and 100, because affection needs to be mutual. After adding a little correction for margin of erorr, in the case where we have a small number of questions, like we do in this example, we're good to go. Any time OkCupid mhetacs two people, it goes through the steps we just outlined. First it collects data about your awrenss, then it compares your choices and preferences to other people's 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. I hope this will help you do that.

Open Cloze

Hello, my name is Christian Rudder, and I was one of the founders of OkCupid. It's now one of the biggest ______ sites in the United States. Like most everyone at the site, I was a math major, As you may expect, we're known for the analytic approach we take to love. We call it our matching algorithm. Basically, OkCupid's ________ 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 _______. 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 __________ even important? Why does this ______ even exist? Well, ______ one very ___________ phrase I used above: they are a step-by-step way to solve a problem, and as you probably know, _________ _____ at step-by-step processes. A computer without an algorithm is basically an expensive paperweight. And since computers are such a _________ part of everyday life, algorithms are everywhere. The math behind OkCupid's matching algorithm is surprisingly simple. It's just some ________, multiplication, a little bit of square roots. The tricky part in _________ it was figuring out how to take something __________, human attraction, and break it into components that a computer can work with. The first thing we needed to match ______ 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 OkCupid should ask _____ questions, stuff like, "Do you want to have kids one day?" "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 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, they're going to have massive problems. We realized this early on, and so we _______ we needed a bit more data from each question. We had to ask people to specify not only their own answer, 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 ______ 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 _______ 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: first, your answer; second, how you want someone else — your potential _____ — to answer; and third, how important the question is to you at all. With all this information, OkCupid can figure out how well two people will get along. The _________ 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 _____ on questions you've both answered. Let's call that set of common _________ "s." As a very simple example, we use a _____ 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, _______ and very _________. And let's say you ________ "very organized," and you'd like someone else to ______ "very organized," and the question is very important to you. Basically, you're 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, from our previous example: "Do you like to be the center of attention?" The answers are "yes" and "no." You've answered "no," you want someone else to answer "no," and the question 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 _________ to him. So, let's try to _______ all of this. Our first step is, since we use computers to do this, we need to assign numerical values to _____ like "somewhat important" and "very important," because computers need everything in numbers. We at OkCupid 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 _____ on your scale? Well, you indicated that B's answer to the first question, about _________, was very important to you. It's _____ 50 points and B got that right. The second ________ is worth only 1, because you said it was only a little important. B got that wrong, so B's answers were 50 out of 51 possible points. That's 98% ____________. Pretty good. The second question the algorithm looks at is: How much did you satisfy B? Well, B placed 1 point on your answer to the messiness question and 10 on your answer to the second. Of those 11, that's 1 plus 10, you earned 10 — you guys satisfied each other on the second question. So your answers were 10 out of 11 ______ 91 _______ satisfactory to B. That's not bad. The final step is to take these two match percentages and get one number for the both of you. To do this, the algorithm __________ 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 percent times 91 percent. That equals 94 percent. That 94 percent is your match percentage with B. It's a mathematical __________ of how happy you'd be with each other, based on what we know. Now, why does the algorithm ________, 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. It's a great way to _______ values that have wide ranges and represent very different properties. In other words, it's perfect for romantic matching. You've got wide ______ and you've got tons of different data points, like I said, about movies, politics, religion — everything. Intuitively, too, this makes sense. Two people satisfying each other 50 percent should be a better match than two others who _______ 0 and 100, because affection needs to be mutual. After adding a little correction for margin of _____, in the case where we have a small number of questions, like we do in this example, we're good to go. Any time OkCupid _______ two people, it goes through the steps we just outlined. First it collects data about your _______, then it compares your choices and preferences to other people's 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. I hope this will help you do that.

Solution

okcupid
organized
percent
people
average
decided
designing
score
error
notice
multiplies
answer
problem
messiness
lesson
equals
satisfactory
ideas
ranges
important
expression
answers
dating
matches
small
combine
compute
algorithm
addition
multiply
computers
match
significant
questions
satisfy
mysterious
question
based
users
pervasive
person
algorithms
matching
excel
worth
answered

Original Text

Hello, my name is Christian Rudder, and I was one of the founders of OkCupid. It's now one of the biggest dating sites in the United States. Like most everyone at the site, I was a math major, As you may expect, we're known for the analytic approach we take to love. We call it our matching algorithm. Basically, OkCupid'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 OkCupid's matching algorithm is surprisingly simple. It's just some addition, multiplication, a little bit of square roots. The tricky part in designing it was figuring out how to take something mysterious, human attraction, and break it into components that a computer can work with. 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 OkCupid should ask users questions, stuff like, "Do you want to have kids one day?" "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 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, they're going to have massive problems. We realized this early on, and so we decided we needed a bit more data from each question. We had to ask people to specify not only their own answer, 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 OkCupid 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: first, your answer; second, how you want someone else — your potential match — to answer; and third, how important the question is to you at all. With all this information, OkCupid 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 questions you've both answered. 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're 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, from our previous example: "Do you like to be the center of attention?" The answers are "yes" and "no." You've answered "no," you want someone else to answer "no," and the question 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 OkCupid 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. B got that wrong, so B's answers were 50 out of 51 possible points. That's 98% satisfactory. Pretty good. The second question the algorithm looks at is: How much did you satisfy B? Well, B placed 1 point on your answer to the messiness question and 10 on your answer to the second. Of those 11, that's 1 plus 10, you earned 10 — you guys satisfied each other on the second question. So your answers were 10 out of 11 equals 91 percent satisfactory to B. That's not bad. 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 percent times 91 percent. That equals 94 percent. That 94 percent 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. It's 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, politics, religion — everything. Intuitively, too, this makes sense. Two people satisfying each other 50 percent 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 where we have a small number of questions, like we do in this example, we're good to go. Any time OkCupid matches two people, it goes through the steps we just outlined. First it collects data about your answers, then it compares your choices and preferences to other people's 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. I hope this will help you do that.

Frequently Occurring Word Combinations

ngrams of length 2

collocation	frequency
matching algorithm	3
match percentage	3
wide ranges	2

Important Words

ability
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adding
addition
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algorithms
analytic
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