American Express Slides, MLconf 2013

MLConf, San Francisco, CA November 15, 20131

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

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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

abhijit.bose@aexp.com

Henry YuanDirector, Data Science

http://www.linkedin.com/pub/henry-yuan/4/29b/9bbhenry.h.yuan@aexp.com

Huiming QuSr. Data Scientist, Data Science & Engineering

http://www.linkedin.com/pub/huiming-qu/4/400/b82huiming.qu@aexp.com