初級 美國腔 24011 分類 收藏
開始影片後,點擊或框選字幕可以立即查詢單字
字庫載入中…
回報字幕錯誤
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.
It's just some addition,
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.
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 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:
first, your answer;
second, how you want someone else,
your potential match,
to answer;
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
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 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.
Now you've answered "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.
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 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,
about politics,
about religion,
about everything.
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.
First it collects data about your answers,
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.
I hope this will help you do that.
    您必須登入才有此功能
提示:點選文章或是影片下面的字幕單字,可以直接快速翻譯喔!

載入中…

【TED-Ed】線上約會的數學原理 (Inside OKCupid: The math of online dating - Christian Rudder)

24011 分類 收藏
Why Why 發佈於 2017 年 12 月 29 日

影片簡介

展開內容
你是線上約會 app 的愛用者嗎?想知道這些 app 是怎麼利用簡單的個人資訊幫你媒合網友、找到對象的嗎?想知道速配指數是如何計算的嗎?快來看看美國最熱門的交友網站 OkCupid 是如何量化個人特質及喜好、計算分數並且為你找到最速配的心靈伴侶!

1algorithm0:30
algorithm 的意思是「演算法」,即是將數學應用到現實生活當中,將生活情境寫成一系列步驟的數學函式、指令。
在現代社會中,最常指稱透過程式語言的方式讓電腦有系統地去執行大量的計算。而生活中最常見的例子當然就是我們都聽過的「臉書的演算法」、「Google 搜尋的演算法」囉!
Google is now renewing its algorithm to make sure those who pay for ads gain more exposure.
Google 現在正在更新它的演算法以確保付費廣告的用戶獲得更多曝光。

The Facebook algorithm is driving me crazy. I'm tired of seeing the same kinds of content every day.
臉書的演算法快逼瘋我了,我實在不想每天打開臉書都看到相同類型的資訊。


*同場加映:
【TED-Ed】機器可以分析我們的臉部表情嗎? (Can machines read your emotions? - Kostas Karpouzis)


2pervasive1:14
pervasive 有三種詞性。作為形容詞有「普遍的;擴大的;滲透的;瀰漫的」的意思,作為副詞有「無所不在地;遍布地」的意思,而作名詞則為「無處不在;遍布」。要注意的是,不論哪一種詞性,pervasive 雖然可以用於中性、甚至是正面的情況,但大多數的時候都是使用於「負面」的影響或實質效果時,例如:

> pervasive ruling power 滲透的統治權力
> pervasive stink 瀰漫的惡臭
> pervasive disaster 遍布的災難
I can stand this pervasive corruption anymore, the government must be overthrown as soon as possible.
我無法再忍受這種無所不在的腐敗了,我們必須及早推翻政府。

The pervasive phenomenon for high school students to live stream publicly is exposing themselves to a high personal safety risks.
中學生間盛行公開直播的現象使他們將自己暴露於很高的人生安全風險當中。


*同場加映:
我們每個人都註定要孤獨? (Why We are Fated to be Lonely)


3mandatory2:48
mandatory 的意思是「強制的;命令的;法定的;必須履行的;要求的」,而意思基本上與其相同的 compulsory 則有「必須做的;有義務的」。這兩個詞彙在大多數的情形都是可以通用的,但其實意思上還是有些許的不同。
mandatory 使用於其強制性是「被規定的」,因此,也很常用於「法律、規範條例」上,例如:

> mandatory sentence 法定判決
> mandatory retirement 法定退休年齡
> mandatory disclosure 法定揭露

compulsory 則用於其強制性是因為這件事「本身是必要的、不可或缺的」,常用於「教育、商務及雇傭關係」之上,例如:

> compulsory education 義務教育
> compulsory military service 義務兵役
> compulsory insurance 強制保險
It's mandatory to hand in the paper immediately after the bell ring.
試卷被規定必須在鐘響之後馬上繳交。

The board is having a meeting now, discussing whether to make the 5-day month off mandatory.
董事會現在正在開會,討論是否要讓月休五日強制化。


*同場加映:
一則推特訊息將如何摧毀你 (How One Tweet Can Ruin Your Life | Jon Ronson | TED Talks)


4crunch3:08
crunch 雖然作動詞,但唸法本身就帶了一點狀聲感覺,意思是「嘎吱嘎吱地咬;嘎吱嘎吱碾碎」,不過,在財經上的另外一個重要用法則是「(財經上) 緊縮、擠壓」。因此若作為名詞,則有「嘎吱聲;危機;經濟緊縮」等意思。
而這部影片中 crunch (the) numbers 的用法則是 20 世紀後葉電腦出現之後才才有片語,意思是「大量的數值運算」,其中包含資訊的蒐集、彙整、計算、分析等。
The asian financial markets take years to recover from the crunches of the 1997 financial crisis.
在 1997 金融風暴後,亞洲金融市場花了好幾年才從危機中復甦。

They've been crunching the numbers for 10 hours in the lab. I believe they should take a break right now.
他們已經在實驗室裡花了十個小時進行大量的數值運算,我想他們現在是時候休息一下了。


*同場加映:
你敢不敢! (I Dare You: Jumping Bellyflop! (ft. Ricegum))


5phenomena7:02
phenomenaphenomenon 的其中一個複數形,意思是「現象」,尤其用於觀察到的現象成因不明、無法解釋時。
phenomenon 還有「奇蹟;傑出的人才」的意思,要注意的是在這個解釋之下,phenomenon 的複數形就會是 phenomenons,直接加上 s 所形成。
The mysterious phenomenon that people whoever step into the mansion would broke their legs in a month attracts numbers of daring people to this small town to challenge it.
所有踏入這棟宅邸的人都會在一個月內摔斷腿的神秘現象吸引了大批大膽的人來到這個小鎮挑戰它。

It's hard to believe what just happened. Is that some sort of popular adolescent phenomenon?
實在很難相信剛剛發生了什麼事。那是某種青少年間流行的現象嗎?


*同場加映:
我們為什麼會哭? Why Do We Cry?


看完之後是不是對於數學在生活中的應用感到十分神奇呢?雖然其中的計算只是簡單的加減乘除加上開根號,但其實每一個步驟都有其背後的道理,實在不得不佩服這些人將數學和生活結合的能力阿!

文/ Lilian Chang

影片學習單字重點

loading
看更多推薦影片

影片討論

載入中…
  1. 1. 單字查詢

    在字幕上選取單字即可即時查詢單字喔!

  2. 2. 單句重複播放

    可重複聽取一句單句,加強聽力!

  3. 3. 使用快速鍵

    使用影片快速鍵,讓學習更有效率!

  4. 4. 關閉語言字幕

    進階版練習可關閉字幕純聽英文哦!

  5. 5. 內嵌播放器

    可以將英文字幕學習播放器內嵌到部落格等地方喔

  6. 6. 展開播放器

    可隱藏右方全文及字典欄位,觀看影片更舒適!

  1. 英文聽力測驗

    挑戰字幕英文聽力測驗!

  1. 點擊展開筆記本讓你看的更舒服

  1. UrbanDictionary 俚語字典整合查詢。一般字典查詢不到你滿意的解譯,不妨使用「俚語字典」,或許會讓你有滿意的答案喔