EHarmony Matching Steve Carter, Ph.D. VP of Matching

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

$

$$

$$$

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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, Ph.D.scarter@eharmony.com