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

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Presentation on theme: "EHarmony Matching Steve Carter, Ph.D. VP of Matching."— Presentation transcript:

1 eHarmony Matching Steve Carter, Ph.D. VP of Matching

2 What is compatibility, and why should I care?

3 Divorce Rate by Length of Marriage and Marriage Date Date of Marriage Length of Marriage

4 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.

5 What do people do well (and poorly)? – Tasks and problems with readily available or proximal information – 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

6 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?





11 Example Compatibility Model

12 I’m compatible with HIM?!?

13 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?

14 AFFINITY = Attraction COMPATIBLITY = Long Term Success

15 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 15

16 What tools do we use?

17 Modeling Tools: Logistic Regression Gradient Boosted Random Forest Visualization Tools: ggplot binom heatmap misc tests RStudio server: 1TB Modeling Exploration: R Studio

18 Modeling feature discovery: Eureqa

19 Modeling: Vowpal Wabbit (aka “vee- dub”)

20 20

21 User Data RQ Answers eHarmony Process Flow – Online Matching Display Matches Compatibility Scoring Pairings Data Affinity Scoring Match Data Complete Profile Process Self- Select Criteria Match Selection Upload Photo My Matches Page Register

22 eHarmony Process Flow – Offline Matching 22 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 AB CD

23 What are the most powerful features in modeling attraction?

24 Prob( ) Distance vs. Probability of Communication Distance between Users in Miles

25 Prob( ) 4 - 8 in cm Height vs. Probability of Communication

26 Prob( ) Self-Rated Attractiveness vs. Communication

27 27 Number of users Profile Photo(s) Aspect Ratio

28 Profile Photo(s) Aspect Ratio vs. Communication

29 Frequency of Users Zoom level Profile Photo(s) Zoom Level

30 Profile Photo(s) Zoom Level vs. Communication

31 Constrain choices to High Quality alternatives Deliver the right matches to the right people at the right time Optimize models based on user behaviorFind and integrate new predictive features Primary Matching Objectives:


33 Double the proportion of Great Marriages Cut the divorce rate in half Double the proportion of highly engaged workers Cut the rate of churn in half


35 *** Gallup-Healthways Well-Being Index


37 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

38 Attracting Candidates Screening Applicants Out Hiring Decisions Organization Optimization Where are “compatibility” tools prevalent? Job Boards ATS Systems Consultants & Assess. Firms HR & Managers

39 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.

40 Features Used in our Predictive Models Personality Factors Aggressiveness Agreeableness Athleticism Attachment/Autonomy Collaboration Conscientiousness Emotional Stability Empathy Extraversion Openness Positive Affect Self Esteem Social Orientation Company Culture/User Values Factors Autonomy/Independent Thinking Communicative Leadership Company Stability Daily Perks Daily Stability Environmental Consciousness Innovation Market Position Motivational Opportunity for Growth Orderliness Playfulness Prestige Respect for Employees Serenity Socially Responsible Team Spirit Work Complexity Work-Life Balance

41 Predictive Compatibility Scoring System User Personality Questionnaire User Values Questionnaire Organization Culture/Values Questionnaire Work Satisfaction Questionnaire User Profiles Organization and Type Profiles Predicted Work Satisfaction Survey and User Data Predictive Models X = User Profiles

42 Conceptual Predictive Model For Personality Factors A – F And Personal Values Factors G – K And Organization Culture Factors L - P Job Compatibility = f [(A u )(G U-u )b i + … + (K i )(P U-u ) b i ] Where dependent measure for training weights b i-k = Job Satisfaction and Performance (some main effects may be partialed-out/controlled)

43 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.

44 What does all this get you?

45 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

46 On-Site Behavioral Features for Machine Learning eHarmony/DatingJob Search Board 1email bounce 2email open 3login 4upload photo 5add profile infoadd resume info 6change profile infochange resume info 7change search parameters 8profile view (top-level click)job listing view (top-level click) 9profile discardjob listing discard 10profile savejob listing save 11communicate 11Ainitiateclick-thru to resume submit 11Brespondsubmit resume 11Creceive initiationreceive phone interview 11Dreceive responseon-site interview 12subscribe 13renew 14resubscribe 15close 15Afrustrated 15Bin relationshiphired

47 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

48 Data capture and iterative modeling Pairs Matches Create Compatible Pairings Score Pairings for Affinity & Value Observe Outcomes Select Matches User Behaviors Update Models Deliver Matches Run-TimeOffline Modeling

49 Investing in “Big Data” Vowpal Wabbit

50 Can it work?

51 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) 51

52 Marital Satisfaction by Meeting Place 52 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 IIIII I IIIII Pairwise comparison to eHarmony MeanStd.DevCountMean Diff.FSig. eHarmony5.860.81714 Other Dating5.631.032068-0.2329.540.00 Other Online5.611.015491-0.2642.230.00 All Other5.521.0716849-0.3472.000.00

53 What’s the rate of separation or divorce? 53

54 Anything you do for love will always be better than everything you do for money.


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