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 fdurenos of OkCupid. It's now one of the bgsgiet dating sites in the United setats. Like most everyone at the site, I was a math major, As you may epecxt, 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 poeple should go on a date. We built our entire bnseusis around it. Now, algorithm is a fancy word, and people like to drop it like it's this big thing. But really, an ahrtgoilm is just a systematic, step-by-step way to solve a peorlbm. It doesn't have to be facny 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 aitomrhgls even itmnoarpt? Why does this lesson even exsit? 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 psroecses. A computer without an algorithm is bscalilay an expensive paperweight. And since computers are such a prsveavie 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 wntead from someone else. That wkoerd 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 bruning or flag burning?" might reveal more about someone than their tatse in mveios. And it doesn't make sense to weigh all things elaquly, so we added one fianl 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 ptotianel 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 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 besad 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 awreesnd "very organized," and you'd like someone else to answer "very organized," and the qitosuen 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 iades like "somewhat important" and "very important," because computers need everything in numbers. We at okuipcd 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 wtroh 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 wnrog, so B's answers were 50 out of 51 possible points. That's 98% stsictforaay. Pretty good. The second question the algorithm looks at is: How much did you sitafsy 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 pnecert satisfactory to B. That's not bad. The final step is to take these two match pgrneteceas 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 smalpe, is only 2, we have: match percentage eqlaus the square root of 98 percent tiems 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 mlplituy, 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 peretoiprs. In other wdors, it's perfect for romantic miachntg. You've got wide ranges and you've got tons of different data points, like I said, about movies, pltiicos, religion — everything. Intuitively, too, this makes sense. Two people sfatisynig 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 nmuebr of qinutesos, like we do in this example, we're good to go. Any time OkCupid matches two people, it goes through the stpes we just outlined. First it collects data about your answers, then it compares your choices and preferences to other people's in smpile, mathematical ways. This, the ability to take real-world pohennema 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 psoren, you use algorithms to tell a stroy to a ctempour. 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 ________ of OkCupid. It's now one of the _______ dating sites in the United ______. Like most everyone at the site, I was a math major, As you may ______, 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 ______ should go on a date. We built our entire ________ around it. Now, algorithm is a fancy word, and people like to drop it like it's this big thing. But really, an _________ is just a systematic, step-by-step way to solve a _______. It doesn't have to be _____ 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 _________? Why does this lesson even _____? 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 _________. A computer without an algorithm is _________ 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 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 ______ from someone else. That ______ 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 _______ or flag burning?" might reveal more about someone than their _____ in ______. And it doesn't make sense to weigh all things _______, so we added one _____ 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 _________ _____ — 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 _____ 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 ________ "very organized," and you'd like someone else to answer "very organized," and the ________ 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 _____ like "somewhat important" and "very important," because computers need everything in numbers. We at _______ 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 _____ 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 _____, 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 _______ 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 _______ satisfactory to B. That's not bad. The final step is to take these two match ___________ 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 ______, is only 2, we have: match percentage ______ the square root of 98 percent _____ 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 ________, 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 __________. In other _____, it's perfect for romantic ________. You've got wide ranges and you've got tons of different data points, like I said, about movies, ________, religion — everything. Intuitively, too, this makes sense. Two people __________ 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 ______ of _________, like we do in this example, we're good to go. Any time OkCupid matches two people, it goes through the _____ we just outlined. First it collects data about your answers, then it compares your choices and preferences to other people's in ______, mathematical ways. This, the ability to take real-world _________ 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 ______, you use algorithms to tell a _____ to a ________. If you learn the language, you can go out and tell your stories. I hope this will help you do that.

Solution

potential
business
burning
matching
people
questions
percent
properties
processes
okcupid
ideas
problem
match
question
equals
story
states
satisfy
simple
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number
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wrong
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expect
based
times
basically
movies
percentages
phenomena
fancy
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answered
founders
important
equally
politics
taste
algorithm
exist
algorithms
person
wanted
multiply
final
computer
satisfying
worth
satisfactory

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

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