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eHarmony MatchingSteve Carter, Ph.D.
VP of Matching
What is compatibility, and why should I care?
Divorce Rate by Length of Marriage and Marriage Date
Date of Marriage
Length of Marriage
• They solve the wrong problem.
Why do people choose mates poorly?
If a donut costs 50 cents and you have $2.50,
how long will it take you to get to Riverside?
Riverside is: • Accessible via the 60 FWY
• 50 miles away
• Full of meth labs
• They use the wrong information.
What do people do well (and poorly)?
– Tasks and problems with readily available or proximal information
EASY
HARD – Tasks and problems involving obscure or distal information
– In these cases, people often use irrelevant, but more readily available information
– Even worse, people may choose to solve the wrong problem, especially in the ‘real world’ where appropriate goals are often unclear
Compatibility requires solving the right problem,and using the right information.
• People focus on attraction rather than worry about long term success.• People make their selections based on the proximal or nearby information
– appearance– location– social information (i.e., things that are polite and interesting and/or that you or
they hope will make a good impression).
• These things may be important when it comes to relationship formation, they just aren't important when it comes to long-term relationship success.
• In contrast, compatibility over the long-term is based on distal and hard-to-acquire information– What are our goals?– How well will our personalities, values and interests mesh? – How often will we disagree over important choices? – How will we communicate with one another when we are angry?
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CMS 2.0 Linear 1 AUS Validation% 4th Qrt
Rem %
cum 4th-Qrt Marriages
Cum Pop
Example Compatibility Model
I’m compatible with HIM?!?
Remember: People generally choose a mate based on elements of attraction largely unrelated to long-term success
• Poor choices are likely to “look good”
• Good choices are likely to “look bad”
You can improve outcomes by limiting choices to a “safe set” of alternatives (i.e., suppressing access to poor choices)
However, you can’t improve marital outcomes by limiting access to poor choices if people don’t like the good choices.
Using machine learned algorithms, the Affinity System strives to satisfy subjective criteria in order to initiate a relationship.
Compatibility System constrains pairings to what you need, Affinity System optimizes matching on what you want.
When isn’t compatibility enough?
AFFINITY = Attraction
COMPATIBLITY = Long Term Success
How do we measure and model attraction?
• Traditionally, we have viewed mutual communication between two users as the definition of a ‘successful’ match– This metric is nicely observable
in our online system
– This behavior fits nicely with the idea of mutual attraction
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What tools do we use?
Modeling Tools:Logistic RegressionGradient BoostedRandom Forest
Visualization Tools:ggplotbinomheatmapmisc tests
RStudio server: 1TB
Modeling Exploration: R Studio
Modeling feature discovery: Eureqa
Modeling: Vowpal Wabbit (aka “vee-dub”)
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User Data
RQ Answers
eHarmony Process Flow – Online Matching
DisplayMatches
Compatibility Scoring
Pairings Data
Affinity Scoring
Match Data
Complete Profile
Process Self-Select Criteria
Match Selection
Upload Photo
My Matches PageRegister
eHarmony Process Flow – Offline Matching
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Compatible pairs are
considered for delivery
Input Pairs
How attractive is the match?
Calculate Utility of
Pairs
How many matches is each user allowed to get today
Apply Business
Rules
Select set that
maximizes sum of B
Select Delivery
Set
A B C D
What are the most powerful features in modeling attraction?
Prob( )
Distance vs. Probability of Communication
Distance between Users in Miles
Prob( ) 4 - 8 in
cm
Height vs. Probability of Communication
Prob( )
Self-Rated Attractiveness vs. Communication
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Num
ber o
f use
rs
Profile Photo(s) Aspect Ratio
Profile Photo(s) Aspect Ratio vs. Communication
Freq
uenc
y of
Use
rsZoom levelProfile Photo(s) Zoom Level
Profile Photo(s) Zoom Level vs. Communication
Constrain choices to High Quality alternatives
Deliver the right matches to the right people at the right time
Optimize models based on user behavior
Find and integrate new predictive features
Primary Matching Objectives:
Double the proportion of Great Marriages
Cut the divorce ratein half
Double the proportion of highly engaged workers
Cut the rate of churnin half
*** Gallup-Healthways Well-Being Index
Workplace Compatibility• Unlike the world of romance, the world of business has long
embraced “compatibility” between workers and jobs as important.• Assessments have a long history of being leveraged for creating a
better fit between employees and their jobs so as to lower training costs, increase performance and productivity, decrease intra-organization conflict and churn and improve overall profitability:– Intelligence testing
– Aptitude assessment
– Skills Matching/Competence Testing
– Culture and Values matching
• The concept of compatibility in job placement and hiring is a permanent part of the vernacular– Overqualified or Under-qualified
– Good or Poor Fit for the organization
Attracting Candidates
Screening Applicants
Out
Hiring Decisions
OrganizationOptimization
Where are “compatibility” tools prevalent?
Job Boards
ATS Systems
Consultants &Assess. FirmsHR & Managers
$
$$
$$$
$$$$
Values, Culture and Personality Matching
• We have leveraged our experience in relationship matching to create compatibility models to match workers and employment.
• In addition to users of the new product, this IP will leverage strategic partnerships with companies, eHarmony users and the internet-at-large to gather data from a broad range of individuals that describes:
– Their personality and values– The culture at their current place of work– Descriptions of their role and type of company– Their level of job satisfaction and engagement
• These individual and company profiles will form the core of our values, culture and personality compatibility scoring system.
Features Used in our Predictive Models
Personality FactorsAggressivenessAgreeablenessAthleticismAttachment/AutonomyCollaborationConscientiousnessEmotional Stability EmpathyExtraversionOpennessPositive AffectSelf EsteemSocial Orientation
Company Culture/User Values FactorsAutonomy/Independent ThinkingCommunicative LeadershipCompany StabilityDaily PerksDaily StabilityEnvironmental ConsciousnessInnovationMarket PositionMotivationalOpportunity for GrowthOrderlinessPlayfulnessPrestige
Respect for EmployeesSerenitySocially ResponsibleTeam SpiritWork ComplexityWork-Life Balance
Predictive Compatibility Scoring System
User Personality Questionnaire
User Values Questionnaire
OrganizationCulture/Values Questionnaire
Work Satisfaction
Questionnaire
User Profiles
Organization and Type Profiles
Predicted Work Satisfaction
Surveyand User
Data
Predictive Models X =User Profiles
Conceptual Predictive Model
• For Personality Factors A – F• And Personal Values Factors G – K• And Organization Culture Factors L - P• Job Compatibility =
f [(Au)(GU-u)bi + … + (Ki )(PU-u) bi ]
Where dependent measure for training weights b i-k = Job Satisfaction and Performance (some main effects may be partialed-out/controlled)
Scoring Abstraction and Company Taxonomies
Industry
Location
Size
Role
Company
Culture Profiles will be generated at escalating levels of generalization, allowing us to compute compa-tibility scores between users and companies for whom no or insuf-ficient data is currently available.
The relative value of these “general compatibility” scores will be est-imated based on the consistency of feature scores within any level of abstraction (i.e., the standard dev-iation of all scores) and the con-fidence interval for predicted compatibility scores.
What does all this get you?
Will compatibility be enough?
Company|Candidate Affinity
How likely is the Candidate to apply for the job?
How likely is the Recruiter to contact the candidate?
Candidate|Job Listing Affinity
This is where the Big Data and Machine Learning begins
On-Site Behavioral Features for Machine LearningeHarmony/Dating Job Search Board
1 email bounce email bounce2 email open email open3 login login4 upload photo5 add profile info add resume info6 change profile info change resume info7 change search parameters change search parameters8 profile view (top-level click) job listing view (top-level click)9 profile discard job listing discard
10 profile save job listing save11 communicate communicate
11A initiate click-thru to resume submit11B respond submit resume11C receive initiation receive phone interview11D receive response on-site interview
12 subscribe13 renew14 resubscribe15 close close
15A frustrated frustrated15B in relationship hired
The Data ‘We’ Need from Companies to really optimize
using ‘Big Data’• We know:
– Who they view– Who they contact
• We would benefit from knowing– Who do they interview– Who do they hire
(which importantly tells us who they DON’T hire)– How long hired employees stay
Data capture and iterative modeling
Pairs Matches
CreateCompatible
Pairings
Score Pairings for Affinity &
Value
ObserveOutcomes
SelectMatches
User Behaviors
UpdateModels
DeliverMatches
Run-Time Offline Modeling
Investing in “Big Data”
Vowpal Wabbit
Can it work?
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When you break out online and offline methods of meeting, online dating is the most likely way that people have met in the US who married since 2005!
Where are people meeting (2005 – 2012)
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Marital Satisfaction by Meeting Place
In addition to being more prevalent than ever before, scientific research has shown that couples who meet online have significantly better relationships than those who meet offline.
The happiest couples meeting through any means had met on eHarmony!
eHarmony Other Dating Other Online All Other
All non-eHarmony dating sites combined All non-eHarmony online sites combined All on and offline non-eHarmony methods combined
I II III
I II III
Pairwise comparison to eHarmony
Mean Std.Dev Count Mean Diff. F Sig.eHarmony 5.86 0.81 714 Other Dating 5.63 1.03 2068 -0.23 29.54 0.00Other Online 5.61 1.01 5491 -0.26 42.23 0.00All Other 5.52 1.07 16849 -0.34 72.00 0.00
What’s the rate of separation or divorce?
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Anything you do for love will always be better than everything you do for money.
Steve Carter, [email protected]