Love Through Science eHarmony 2015
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Internal proof of fraud from eHarmony.Big Five normative personality test is OBSOLETE (incorrect and incomplete model of personality)
Transcript of Love Through Science eHarmony 2015
WHERE ARE PEOPLE MEETING?
EHARMONY -‐ WHO WE ARE
SOFTWARE ENGINEERS DATA SCIENTISTS PSYCHOLOGISTS
PRODUCT SPECIALISTS MARKETING CUSTOMER CARE
Affinity Matching Match Distribu2on
COMPATIBILITY MATCHING SYSTEM
COMPATIBILITY MATCHING SYSTEM
COMPATIBILITY MATCHING SYSTEM
COMPATIBILITY MATCHING SYSTEM
COMPATIBILITY MATCHING SYSTEM
COMPATIBILITY MATCHING SYSTEM
COMPATIBILITY MATCHING SYSTEM
COMPATIBILITY MATCHING SYSTEM
COMPATIBILITY MATCHING SYSTEM
OUTPUT We are le7 with a number of pairings for each user
Affinity Matching Match Distribu2on
COMPATIBILITY MATCHING SYSTEM
AFFINITY MATCHING
1 Incompa@ble lifestyles
Very compa@ble -‐ communica@on happens
2 Incompa@ble loca@ons
3 Incompa@ble ages
AFFINITY MATCHING
PHOTOS INFORMATION PROFILE INFORMATION
VARIOUS PREFERENCES SITE USAGE
FEATURES
FACIAL DETECTION -‐ VIOLA JONES
P. Viola, M. Jones Rapid Object Detec2on using a Boosted Cascade of Simple Features. CVPR 2001
FACIAL DETECTION -‐ VIOLA JONES
P. Viola, M. Jones Rapid Object Detec2on using a Boosted Cascade of Simple Features. CVPR 2001
FACE DETECTION
IMAGE FEATURES
Aspect Ra2o is defined by
WIDTHHEIGHT = 4.038
Face Ra2o is defined by
FACE AREAIMAGE AREA = 0.177
FACE RATIO
FACE RATIO
FACE RATIO
Score = 5.98 + 100.96*x - 1279.95*x^2 + 6483.85*x^3 -14767.15*x^4 + 12492.38*x^5
Shape(Part Loca@on)App(Image, Part Loca@on) Model(Image, Part Loca@on)+ =
X. Zhu, D. Ramadan Face Detec2on, Pose Es2ma2on and Landmark Localiza2on in the Wild. CVPR 2012
hUp://github.com/eharmony/face-‐parts-‐service
FACE DETECTION
FACE PARTS
AFFINITY MATCHING -‐ FACIAL ANALYSIS
POSEHAIR COLOR
HAS BEARD?
HAS MUSTACHE?
EYE COLOR
AFFINITY MATCHING -‐ NEW PHOTO FEATURES
HAS CLEAVAGE?
AVERAGE EYE WIDTH
CHEEKBONE WIDTH JAW WIDTH
L. Wen, G. Guo A computa2onal approach to body mass index predic2on from face images. Image and Vision Compu2ng, 2013
CHEEKBONE WIDTH UPPER FACE HEIGHT
AFFINITY MATCHING -‐ NEW PHOTO FEATURES
FACE PERIMETER FACE AREA
LOWER FACE HEIGHT FACE HEIGHT
FACE WIDTH LOWER FACE HEIGHT
AVERAGE DISTANCE FROM EYE TO EYEBROW
AFFINITY PROCESS
20%
50%
45%
75%
38%
82%
MODEL THAT PREDICTS COMMUNICATION~10^3 Features
~10^7 Matches per day
~60M registered users
DSL
Compa2bility Matching Affinity Matching Match Distribu2on
COMPATIBILITY MATCHING SYSTEM
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(L1,0)
(L2,0)
(LN,0)
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(LN+1,0)
(LN+2,0)
(LN+M,0)
(1,-‐A1,1)
(1,-‐A1,2)
(1,-‐A2,M)
(1,-‐AN,2)
(1,-‐AN,M)
MATCH DISTRIBUTION -‐ PROBLEM
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(L1,0)
(L2,0)
(LN,0)
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.
(LN+1,0)
(LN+2,0)
(LN+M,0)
(1,-‐A1,1)
(1,-‐A2,M)
(1,-‐AN,2)
MATCH DISTRIBUTION -‐ PROBLEM
MATCH DISTRIBUTION
MATCH GOAL: An amount of matches is assigned
8 MATCHES 6 MATCHES 10 MATCHES
Can we do be_er?
HISTORY -‐ CONTEXTUAL BANDITS
M. Dudki, J. Langford, L. Li, “Doubly Robust Policy Evalua2on and Learning.” ICML, 2011.
MATCH GOALS -‐ CONTEXTUAL BANDITS
8 MATCHES 6 MATCHES 10 MATCHES
This model is used for training
8 MATCHES 6 MATCHES 10 MATCHES
RESULT: BETTER COMMUNICATION
-0.22
-0.213
-0.205
-0.198
-0.19
Constant 6 Constant 7 Constant 8 Constant 9 Constant 10 Uniform Model
MATCH GOALS -‐ CONTEXTUAL BANDITS
WHO’S MARRYING ONLINE?
THE BOTTOM LINE
EHARMONY -‐ THE BIG PICTURE
