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American Express Slides, MLconf 2013

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American Express Slides, MLconf 2013

  1. 1. Recommendations @ American Express Abhijit Bose, Henry H Yuan and Huiming Qu Data Science and Engineering American Express Company MLConf, SanSan Francisco, CA MLConf, Francisco, CA November 15, 2013 2013 1November 15,
  2. 2. American Express Today MLConf, San Francisco, CA MLConf, San Francisco, CA November 15, 2013 November 15, 2013 2
  3. 3. Our closed loop gives us direct relationships with millions of buyers and sellers and a wealth of information about buyers and sellers MLConf, San Francisco, CA November 15, 2013 3
  4. 4. Our products must adhere to the highest standards 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. MLConf, San Francisco, CA November 15, 2013 4
  5. 5. Data Scientists @ American Express Diverse backgrounds (MS, MBA, PhD): computer science electrical engineering physics mechanical engineering statistics economics operations research A mix of American Express talent and alumni Of: and others MLConf, San Francisco, CA November 15, 2013 5
  6. 6. Recommendation opportunities exist in many different channels MLConf, San Francisco, CA November 15, 2013 6
  7. 7. My Offers Mobile App MLConf, San Francisco, CA November 15, 2013 7
  8. 8. https://sync.americanexpress.com/ MLConf, San Francisco, CA November 15, 2013 8
  9. 9. Website Personalization MLConf, San Francisco, CA November 15, 2013 9
  10. 10. Merchant Insight Portal MLConf, San Francisco, CA November 15, 2013 10
  11. 11. What a Typical Transaction Looks Like Merchant Name Merchant Street Address Merchant Zip Code Total Amount Amex card used Transaction ID (useful for history, e.g. returns, tips, etc) Transaction Timestamp MLConf, San Francisco, CA November 15, 2013 11
  12. 12. Recommender Apps Channe l Input Audience Transaction history Customer profile Merchant profile Context MLConf, San Francisco, CA November 15, 2013 12
  13. 13. General Approaches 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. MLConf, San Francisco, CA November 15, 2013 13
  14. 14. Input to MyOffers Find the most relevant merchant offers for our cardmembers, with closed loop data and “real time” context. Transactional History Lifestyle Attributes AXP Internal 19-Nov-13 MLConf, San Francisco, CA November 15, 2013
  15. 15. MyOffers Ecosystem Offer Database Offer Contents Merchant Reporting Batch Hadoop Environment Pre Calculation Expert Rules Fulfillment Real Time Solr Contextual Information CM Channels Synced Card MLConf, San Francisco, CA November 15, 2013
  16. 16. Lessons Learnt •Agile development for shorter cycle •Platform and software challenges •Noisy signals, e.g. taxicabs •Cold-start issue •Local vs. Online MLConf, San Francisco, CA November 15, 2013 16
  17. 17. Lesson Learnt – Geo-Fencing is Critical MLConf, San Francisco, CA November 15, 2013 17
  18. 18. 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 MLConf, San Francisco, CA November 15, 2013 18
  19. 19. Technologies d3.js Custom ML Implementations MLConf, San Francisco, CA November 15, 2013 19
  20. 20. We are Hiring! Build the next generation of: - Recommendation systems - Graph Algorithms - Machine Learning algorithms for Marketing, Fraud and a variety of problems - Data products - Experiments MLConf, San Francisco, CA November 15, 2013 20
  21. 21. Please Contact us at: Abhijit Bose VP, Data Science & Engineering http://www.linkedin.com/in/abose abhijit.bose@aexp.com Henry Yuan Director, Data Science http://www.linkedin.com/pub/henry-yuan/4/29b/9bb henry.h.yuan@aexp.com Huiming Qu Sr. Data Scientist, Data Science & Engineering http://www.linkedin.com/pub/huiming-qu/4/400/b82 huiming.qu@aexp.com MLConf, San Francisco, CA November 15, 2013 21

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