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Recommendations @ American Express
Abhijit Bose, Henry H Yuan and Huiming Qu
Data Science and EngineeringAmerican Express Company
MLConf, San Francisco, CA November 15, 2013
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American Express Today
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Our closed loop gives us direct relationships with millions of buyers and sellers
and a wealth of informationabout buyers and sellers
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Trust and security have been the hallmarks of the American Express brand for more than 160 years.
Turning good data into more tailored and targeted commerce does not change our privacy policies and principles.
We know customers need transparency and clear explanations.
We use data to better serve our customers. We do not sell personally identifiable information in any context.
Our products must adhere to the highest standards
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Data Scientists @ American ExpressDiverse backgrounds (MS, MBA, PhD):
economics
computer scienceelectrical engineeringphysicsstatisticsmechanical engineering
A mix of American Express talent and alumni Of:
and others
operations research
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Recommendation opportunities exist in
many different channels
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My Offers Mobile App
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https://sync.americanexpress.com/
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Website Personalization
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Merchant Insight Portal
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Merchant Name
Merchant Street Address
Total Amount
Amex card used
Merchant Zip Code
Transaction Timestamp
Transaction ID (useful for history, e.g. returns, tips, etc)
What a Typical Transaction Looks Like
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Recommender Apps
Transaction history
Customer profile
Merchant profile
Context
InputChannel
Audience
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Collaborative Filtering - Recommend what similar users like explicitly or implicitly.
Content based - Recommend similar items solely based on the content of items.
Hybrid- Combines the above two.
General Approaches
MLConf, San Francisco, CA November 15, 2013
Find the most relevant merchant offers for our cardmembers, with closed loop data and “real time” context.
Transactional History
LifestyleAttributes
Apr 8, 2023AXP Internal
Input to MyOffers
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BatchHadoop Environment
Contextual Information
Real TimeSolr
Offer Database
Offer Contents
CM ChannelsFulfillment
Synced Card
Merchant Reporting
Pre CalculationExpert Rules
MyOffers Ecosystem
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•Agile development for shorter cycle
•Platform and software challenges
•Noisy signals, e.g. taxicabs
•Cold-start issue
•Local vs. Online
Lessons Learnt
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Lesson Learnt – Geo-Fencing is Critical
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Current Focus is to build out an end-to-end platform and a rich experimentation layer
•Centralization of data
•Better algorithms
•Better incorporation of customer feedback
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d3.js
Custom ML Implementations
Technologies
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Build the next generation of:-Recommendation systems-Graph Algorithms -Machine Learning algorithms for Marketing, Fraud and a variety of problems-Data products -Experiments
We are Hiring!
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Please Contact us at:Abhijit Bose
VP, Data Science & Engineeringhttp://www.linkedin.com/in/abose
Henry YuanDirector, Data Science
http://www.linkedin.com/pub/henry-yuan/4/29b/[email protected]
Huiming QuSr. Data Scientist, Data Science & Engineering
http://www.linkedin.com/pub/huiming-qu/4/400/[email protected]
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