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Wakoopa Recommendations Engine on AWS

Menno van der Sman, Lead Engineer of Wakoopa presents at the AWS Start-Up Event - Amsterdam about their use of Amazon EC2 and S3 for their recommendations engine.

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Wakoopa Recommendations Engine on AWS
Menno van der Sman
       Lead Developer


   Coen Stevens
   Recommendation Engineer
Mission:
Discover software & games
Updates
Searching




            powered by
Recommendations




   Codename: Ludwig

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Wakoopa Recommendations Engine on AWS

  • 2. Menno van der Sman Lead Developer Coen Stevens Recommendation Engineer
  • 5. Searching powered by
  • 6. Recommendations Codename: Ludwig
  • 7. How to get started? Research Mathemagicians Amazon, Netflix etc Peter Tegelaar & Coen Stevens Ludwig created recommender system in ruby running on EC2
  • 8. Challenges when building your first recommender system
  • 9. Data what do we have? Usage (implicit) vs. Ratings (explicit) • Noisy • Accurate • Only positive • Positive and negative feedback feedback • Easy to collect • Hard to collect
  • 10. Item-Based Collaborative Filtering User software usage matrix Software items 220 90 180 22 280 12 42 80 Users 175 210 210 45 165 14 35 195 13 25 100 50 185 35 190 60 65 185
  • 11. Classified user software usage matrix (1, 2, 3) Software items 3 2 2 2 3 2 1 2 Users 3 3 2 3 2 1 2 2 3 2 3 2 2 2 3 1 2 3
  • 12. How do we predict the probability that I would like to use GMail? Software items 3 2 2 2 3 2 1 2 Users 3 3 ? 2 3 2 1 2 2 3 2 3 2 2 2 3 1 2 3
  • 13. Calculate the similarities between Gmail and the other software items. Software items 3 2 2 2 3 2 1 2 Users 3 3 2 3 2 1 2 2 3 2 3 2 2 2 3 1 2 3 Similarity(Firefox, Gmail)
  • 14. Calculate the similarities between Gmail and the other software items. Gmail similarities 0.6 3 2 2 2 0.8 3 2 1 2 1.0 3 3 2 3 0.4 2 1 2 2 3 2 0.4 3 2 2 2 3 0.3 1 2 3 0.3
  • 15. Calculate the predicted value for Gmail Gmail similarities User usage 0.6 3 0.8 3 1.0 0.4 2 0.4 0.3 3 0.3
  • 16. Calculate the predicted value for Gmail Gmail similarities User usage We take only the ‘K’ most similar items (say 2) 0.6 3 0.8 3 1.0 0.4 2 0.4 0.3 3 0.6*3 + 0.8*3 = 2.8 0.6 + 0.8 + 0.4 + 0.3 0.3
  • 17. Calculate all unknown values and show the Top-N recommendations to each user Software items 3 2 ? 2 ? ? 2 3 2 1 ? 2 ? ? Users 3 3 ? 2 ? 3 ? 2 1 2 2 3 2 ? ? 3 2 2 ? 2 3 ? 1 ? 2 ? ? 3
  • 18. Metrics measure for success Space complexity: O(m + Kn) Computational complexity: O(m + n²) Performance: Root Mean Squared Error
  • 19. Evaluating the approach Maximize ( performance cost ) This is easy with EC2
  • 20. Why EC2? Low cost Flexibility Ease of use
  • 21. Infrastructure Wakoopa EC2 checkout Repository Computing Application power Database ssh tunnel Big Database
  • 22. Want more? http://recked.org Time & place TBD