As Andrew Ng says, AI is the new electricity and it will transform many industries, therefore dating is going to be transform by the use of AI. With the experience of having learned AI during the university several years ago, and having updated it since 2016, plus 10 years working experience in the dating industry, this presentation shows the evolution of AI during these last years, and shows some AI examples already used in Dating services. Then shows where AI can be applied to dating services, what is needed, which models can be used, shows the building process, and how can be done.

1. Artificial Intelligence Use Cases
for International Dating Apps
Lluis Carreras
Mobifriends

3. 3
SINCE 2009
SPANISH AND ENGLISH
MORE THAN 2 MILLION USERS
SPAIN, US AND LATIN AMERICA

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AI - MACHINE LEARNING
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
SUPERVISED
LEARNING
UNSUPERVISED
LEARNING
REINFORCEMENT
LEARNING
● REGRESSION
● CLASSIFICATION
● FIND PATTERNS ● LEARN BY REWARD
AND PUNISHMENT

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2011 - DEEP LEARNING
START THE RISE OF DEEP LEARNING

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GOOGLE AI PROJECTS
SUPERVISED
LEARNING
2011
● REGRESSION
● CLASSIFICATION
● FIND PATTERNS
2012 2013 2014 2015 2016 2017 2018
0
200
400
600
800
1000
1200
1400
1600
GOOGLE
BRAIN TEAM
2014
STARTED
TENSORFLOW
2015
RELEASED
TENSORFLOW
2018
TENSORFLOW
HUB
TENSORFLOW
EXTENDED

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GOOGLE AI PROJECTS
Android Apps Gmail Images Maps
Cloud Translation YoutubeAssistant Robotics

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SOME DATING SERVICES ALREADY USING AI
BADOO
LOOKILIKE
TINDER
LIKEABLE
MEETIC
CHATBOT
MATCH GROUP
Q3 2017
● Largest tech resource in
the category devoted to
machine learning in
order to improve
discovery.
● New AI-driven data
products that
personalize experience
● Enhanced analytics

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AI APPLICATIONS FOR DATING
● CONVERSION PREDICTIONS
● CHURN PREDICTIONS
● DYNAMICS PRICING
● CHATBOT ASSISTANT
● USERS' TEXT MODERATION
● USERS' PHOTOS MODERATION
● DETECTING SCAMMERS/TROLLS
● SIMILARITIES RECOMMENDATIONS
● BIDIRECCIONAL RECOMMENDATIONS

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WHAT YOU NEED TO TRAIN, TEST AND DEPLOY AI
BUSINESS
NEED
DATA
ANALYSIS
DATA
PREPARATION
TRAINING
MODEL
ANALYZE
MODEL
MODEL
SERVING
MONITORING
80% DATA ANALYSIS/PREPARATION - 20% MODELING

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AI – DATA SPLIT
DATA SIZE
TRAIN SET 10.000TEST
60% 20%
DEV
20%
TRAIN SET 100.000
80% 10%10%
D T
TRAIN SET 1.000.000
98% 1%1%
TRAINING SET → Data set used to train the model
DEVELOPMENT SET → To adjust the model
TEST SET → To verify the model (good accuracy and no overfitting)

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AI – INTERNATIONAL DATING DATA SPLIT
Randomly shuffle before splitting.
Dev set and Test set should reflect data you expect to get in the
future and consider important to do well on.
Test set should be big enough to give high confidence in the
overall performance of the model.
Data features should be independent.

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MODELS – FROM SIMPLE TO COMPLEX
Linear Regression
Logistic Regression
Neural Network
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)

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MODELS
Scale drives performance progres
Large NN
Medium NN
Small NN
Tradicional learning
Amount of data
Performance

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MODELS
For some predictions simpler models can be good enough.
Do not use deep learning for all.
Simple model = White box (You can see what it does)
Complex model = Black box (You don't know how it does it)

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ERROR - ACCURACY
ERROR -> % of wrong predictions
ACCURACY = 100% - ERROR
Accuracy measure how good the system is.
It cannot reach 100%. Only can get close to Bayes optimal
error.
We can use the best accuracy that an expert or group of
expert can get as a proxy for Bayes error

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ERROR - ACCURACY
0
10
20
30
40
50
60
70
80
90
100
Accuracy evolution
Bayes optimal error
Accuracy
Human-level
Time
Accuracy

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BUILDING PROCESS
BUSINESS
NEED
REQUIREMENTS
SATISFICING AND
OPTIMIZING
METRICS
ONE EVALUATION
METRIC
MODEL
CODING
MODEL
TRAINING
TEST
EVALUATION
TRY FAST, MEASURE AND ITERATE

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ERROR ANALYSIS
CARRYING OUT ERROR ANALYSIS
Look at dev examples to evaluate ideas
CLEANING UP INCORRECT LABELED DATA
ADDRESS MISMATCHED TRAINING AND DEV/TEST DATA
DECIDE IF YOU SHOULD USE DIFFERENT MODELS FOR SOME
COUNTRIES OR SEGMENTS

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MODELS FOR AI APPLICATIONS FOR DATING
● CONVERSION PREDICTIONS (Logistic Regresion)
● CHURN PREDICTIONS (Logistic Regresion)
● DYNAMICS PRICING (Linear Regresion)
● CHATBOT ASSISTANT (RNN / Natural Language Processing (NLP))
● USERS' TEXT MODERATION (Recurrent NN)
● USERS' PHOTOS MODERATION (Convolutional NN)
● DETECTING SCAMMERS/TROLLS (Anomaly detection)
● SIMILARITIES RECOMMENDATIONS (Collaborative Filtering (CF))
● BIDIRECCIONAL RECOMMENDATIONS (Collaborative Filtering (CF))

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HOW WE CAN DO IT
USE EXTERNAL SOLUTION WHEN:
● Common problem
● Continuous improvement
● Not too expensive
DEVELOP WHEN:
● External solution too expensive for you. ROI << LTV
● Have thousands/millions of good labeled data

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EXTERNAL PROVIDERS
FIND APIs
PHOTOS
Photo moderation Face detection Face Landmarks
Face attributes Face comparing Face searching

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EXTERNAL PROVIDERS
SCAMMER DETECTION
FRAUD DETECTION

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MACHINE LEARNING DEVELOPMENTS
When possible use TRANSFER LEARNING
REUSE EXISTING PRE-TRAINED MODELS

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DEVELOPMENT OPTIONS
● AUTO/NON PROGRAMABLE OPTION
● INHOUSE DEVELOPERS AFTER MOOC TRAINING
● INHOUSE DEVELOPERS WITH EXTERNAL CONSULTANT
Freelance University Research Research Center Company
● HIRE DATA SCIENTIST/DATA ENGINEER
● EXTERNAL DATA SCIENTIST/DATA ENGINEER
● EXTERNAL BIG DATA/MACHINE LEARNING COMPANY
● UNIVERSITY RESEARCH / RESEARCH CENTER

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CONCLUSIONS
1. AI WILL TRANSFORM DATING. GET READY!
2. GOOD LABELED DATA IS THE BASE
3. FOR SOME PREDICTIONS SIMPLE MODELS CAN BE GOOD ENOUGH
4. IF A GOOD SOLUTION EXIST, USE IT!
5. CHOOSE THE DEVELOPMENT OPTION THAT FITS YOU BEST

27. Lluis Carreras @lluis_carreras
Mobifriends lluis.carreras@mobifriends.com
THANK YOU!