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CoMeet Berlin 28.09.2017

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“Recommendation solution: Combining Human Learning & Machine Learning” Uzi Blum

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CoMeet Berlin 28.09.2017

  1. 1. RECOMMENDATION SOLUTION: Combining Human Learning & Machine Learning 1
  2. 2. 2 Uzi Blum VP Business Intelligence & Analytics Analytics Data Driven Application
  3. 3. LAUNCH your app and scale your user base GROW your app with quality users RETAIN your users and maximize your ROI 3 We deliver the right users at the right time at every step of your app marketing lifecycle
  4. 4. HUMAN LEARNING 4 Human learning is the act of acquiring knowledge. It occurs during training and personal experience. This knowledge resides in ones brain.
  5. 5. 5 Can Machine Learning replace Human Learning ? vs.
  6. 6. HUMAN vs. MACHINE 6
  7. 7. Typical Human and Machine Learning Approach 7 Identify Revenue Potential Implement Recommendation solution based on heuristic approach Performance Measuring + Internal Feedback (Human) Adding scoring and feature selection mechanism for ML Scaling à Direct recommendation to clients “King of the Hill” Multi algos HL and ML and HL+ML MONTH 0 MONTH 1 MONTH 2 MONTH 3 MONTH 4 MONTH 5 “Similarity Density”What are the most attractive products for each client? “ May the best Algo Win! “ Random Forest K-Means
  8. 8. Tim Maor Stefan A B C D E SIMILARITY DENSITY 8 PRODUCT CLIENT DENSITY 2/3 2/3 3/3 1/3 1/3 DENSITY 4/5 2/5 3/5
  9. 9. THE “RAINBOW” APPROACH 9 DEVELOPMENT Problem Data Algo/s Feedback & Performance Distribution Method Recommendation List OUTPUT
  10. 10. 10 KEY TAKE AWAYS • Performance and Feedback “What was the performance on the latest recommendation? • Business Impact “How much impact did I make on the business in the last month?” • Velocity “How fast can we create a minimal viable product?”
  11. 11. Selected Partners: 11 Questions? uzi.blum@applift.com | www.AppLift.com

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