Big Dating: Computer Science and Relationships Rahul Simha department of computer science George Washington University Office hours: 1pm Tuesdays, 5pm Wednesdays
some stats Usage of online da-ng websites : (total registra=ons) • OKCupid: 5 million • Chemistry: 11 million • E-harmony: 33 million • POF: 40 million • Tinder: 50 million • Match: 96 million • Badoo: 200 million
your data Exercise 1 Part 1: On the worksheet, write three things about yourself (without iden=fying yourself) that will help you stand out in an online da=ng site Part 2: Write down your height in inches but add a fudge factor of +10 or -10 using the following rule: if you were born in an even-numbered month, add 10. Otherwise subtract 10. Thus, if your height is 67 inches and you were born in March, you would write 57 Follow remaining “snowball” instruc=ons
more stats Percep-on : • 60% of US adults: “online da=ng is a good way to meet people” • 2/3 of online daters have gone on a date with someone they met online • 27% between ages 18-24 have used online da=ng But … 5% of those in commi[ed rela=onships say they met online
why the gap? Exercise 2: At your table, come up with three reasons why online matchups may not lead to commi[ed rela=onships
computer science to the rescue Algorithmic matching : • OKCupid • EHarmony Basic ideas : • Ask lots of ques=ons • Perform some kind of scoring and matching
computer science issues High dimensional data Large data size Privacy, security (height example) Algorithm design
high dimensional data Exercise 3 : draw the points (1,2), (2,1), (2,2), (7,6), (8,7), (9,10), (1,9) on paper. How many clusters do they fall into? Exercise 4 : how many dimensions are present in the survey data you filled?
high dimensional data Exercise 3 : draw the points (1,2), (2,1), (2,2), (7,6), (8,7), (9,10), (1,9) on paper. How many clusters do they fall into? Exercise 4 : how many dimensions are present in the survey data you filled? Algorithmic challenge : effec=ve clustering of high dimensional data E-Harmony: 29 dimensions
the scoring problem Distance measure: • Given any two users, compute “how compa=ble they are” Sort: • Sort all users by compa=bility For every user we now have a sorted list of other users, in order of preference
the matching problem Suppose we need to match people, e.g., H 1 H 2 H 3 with R 1 R 2 R 3 Example matching: H 1 H 2 H 3 R 1 R 2 R 3
the matching problem Suppose we know “spousal” preferences: H 1 `s preferences: R 2 R 1 R 3 H 2 `s preferences: R 2 R 3 R 1 H 3 `s preferences: R 2 R 1 R 3 R 1 `s preferences: H 1 H 2 H 3 R 2 `s preferences: H 3 H 1 H 2 R 3 `s preferences: H 2 H 1 H 3 So H 1 would prefer R 2 as spouse to R 1 and R 1 over R 3
the matching problem Consider Example matching: H 1 `s preferences: R 2 R 1 R 3 H 1 H 2 H 3 H 2 `s preferences: R 2 R 3 R 1 H 3 `s preferences: R 2 R 1 R 3 R 1 R 2 R 3 R 1 `s preferences: H 1 H 2 H 3 R 2 `s preferences: H 3 H 1 H 2 R 3 `s preferences: H 2 H 1 H 3 Exercise 5: what is the problem with this matching?
the matching problem Consider Example matching: H 1 `s preferences: R 2 R 1 R 3 H 1 H 2 H 3 H 2 `s preferences: R 2 R 3 R 1 H 3 `s preferences: R 2 R 1 R 3 R 1 R 2 R 3 R 1 `s preferences: H 1 H 2 H 3 R 2 `s preferences: H 3 H 1 H 2 R 3 `s preferences: H 2 H 1 H 3 H 3 and R 2 will elope! Algorithmic challenge : devise an algorithm to create a stable matching
the proposal algorithm 1. Ini=ally place all H’s in unmarried-list 2. while unmarried-list is not empty 3. H i = lowest numbered from list 4. try R’s in order of H i ’s preference 5. if R j is not matched, match H i and R j 6. else if R j prefers H i to current match then 7. match R j with H i 8. return current match to unmarried list Can prove: provides a stable matching Exercise 6: do the H’s or R’s get the best deal?
the proposal algorithm Demo Prac-cal applica-ons?
the proposal algorithm Demo Prac-cal applica-ons: • Med school internships • Clerkships with judges
the (network) structure of rela-onships Exercise 7: Go to h[ps://oracleomacon.org/ and enter actors in two movies YOU have seen. Try to find two actors with a distance of 4.
the (network) structure of rela-onships The Milgram experiment Demo
the (network) structure of rela-onships The Milgram experiment Demo Facebook: 4.57 (among 1.5b users) Da=ng app based on “who knows who”: Hinge
talk to a bot Exercise 8: Choose between 1. Go to h[p://www.masswerk.at/elizabot/ and converse with Eliza. 2. Talk to Siri and record the exchange on paper. 3. Volunteer as judge. Compe-tors: your conversa=on must be short (two back-and-forths) Judges: pick the best conversa=on. Remember Robert Epstein?
our future with robots Exercise 9: Choose between 1. Yes, it’s fine for humans to marry robots in the future. 2. No, that should never be allowed. Write down your reasons on the worksheet
summary Computer science under the hood: • Programming of websites (for online da=ng) • Servers, networks, clouds, large data: Ø Example: 25 TB of data at E-Harmony, incl. 200+ million images • Algorithms for matching, graph structure • Algorithms for clustering, machine learning, natural languages • Robo=cs / AI
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