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Challenges and research for a real-time recommendation at OLX

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Talk on industry session at LARS 2019

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Challenges and research for a real-time recommendation at OLX

  1. 1. Challenges and Research for Real-Time Recommendation in a Dynamic Marketplace Environment
  2. 2. Some unlimited consumption items
  3. 3. Limited but with stock under control
  4. 4. Unique items and stock out of control
  5. 5. @timotta
  6. 6. contacts influenced by recommendations Largest online classified in Brazil daily users +7M +500k new ads published daily +15%
  7. 7. Heavy ad publishing flow X
  8. 8. Graph based collaborative filtering
  9. 9. Real-time graph updating Adega (PostgreSQL) Sommelier (API) Lurker (Tracker) Stream processor
  10. 10. contacts Great result adviews +6% +4%
  11. 11. Collaborative filtering based on ad views View Contact
  12. 12. Concentration Buyers Sellers :( X X ✓ :( :(
  13. 13. The Idea: A content-based ranked by contact probability
  14. 14. Random item-item balanced dataset Ad Viewed Features Ad Recommended Features Target: Contacted yes or no?
  15. 15. Features: Title and description Title, description and category Doc2Vec embedding
  16. 16. Features: Image Image embedding from ResNet's penultimate layer
  17. 17. Features: Neighborhood Neighborhood latent factors generated by logistic matrix factorization
  18. 18. Features: Price $$$ Price
  19. 19. Classification on a balanced dataset accuracy 75%
  20. 20. Studying how to compare both methods offline . . .
  21. 21. Cannot predict online due to high time loading candidate ads and calculating probabilities
  22. 22. Real-time background prediction Adega (PostgreSQL) Embedding calculations
  23. 23. Real-time background prediction Adega (PostgreSQL) Embedding calculations Probability calculation
  24. 24. Real-time background prediction Adega (PostgreSQL) Embedding calculations Probability calculation Reversed Probability calculation
  25. 25. Real-time background prediction Adega (PostgreSQL) Sommelier (API) Embedding calculations Probability calculation Reversed Probability calculation
  26. 26. Future research News Session-Based Recommendations using Deep Neural Networks (Chamaleon) Metadata Embeddings for User and Item Cold-start Recommendations (lightFM)
  27. 27. Recommendation Squad at OLX Filipe Casal Marcelo Malta Tiago Motta Leonardo Wajnsztok Thays Macedo

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