How to build a Recommender System

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This presentation show the method to build a Recommender System with Collaborative FIltering method.

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  • @leminhhai that's cool :D
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  • cũng không có biết anh Tuấn thuyết trình , nếu không ghé xem rùi
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  • Về recommendation có thể tham khảo rất nhiều tài liệu hay từ http://www.netflixprize.com/ (Năm 2009 Netflix có tổ chức cuộc thi về recommendation, nếu nhóm nào tăng kết quả reommendation lên 10% thì được $1 triệu). Về tối ưu performance thì quả thực là vấn đề đau đầu, nếu có tiền thì dùng AMZ hay Google clould (+ sử dụng thêm Predition api). Có một bài viết khá hay về tính correlations 316 triệu phim của Bo Yang trong vòng 2 phút thay vì 2.5 giờ http://dmnewbie.blogspot.com/2009/06/calculating-316-million-movie.html . Một số recommendation service có thểm tham khảo là: http://www.recomaticapp.com/ http://monetate.com/ - expensive
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  • kết thúc thiếu cái quan trọng là high performance thì làm như nào :D
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  • Ôi, đợi cái Content-based filtering mà kết thúc cụt vậy
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How to build a Recommender System

  1. 1. Recommender System How to build a
  2. 2. Võ Duy Tuấn Technical Director @ dienmay.com  PHP 5 Zend Certified Engineer  Mobile App Developer  Web Developer & Designer  Interest: o PHP o Large System & Data Mining o Web Performance Optimization o Mobile Development
  3. 3. Introduction Collaborative Filtering Question & Answer AGENDA
  4. 4. 1. Introduction
  5. 5. APPLICATIONS • Personalized recommendation • Social recommendation • Item recommendation • Combination of 3 approaches above
  6. 6. AMAZON.COM | BOOKS
  7. 7. PLAY.GOOGLE.COM | APPS
  8. 8. SKILLSHARE.COM | CLASSES
  9. 9. PROCESS DIAGRAM Preprocessing Data Analysis Adjustment INPUT OUTPUT
  10. 10. TYPE OF RECOMMENDER SYSTEM • Collaborative filtering • Content-based filtering • Hybrid
  11. 11. 2. Collaborative Filtering
  12. 12. USER & ITEM
  13. 13. ORDER DATA
  14. 14. ORDER DATA (cont.)
  15. 15. ORDER DATA (cont.)
  16. 16. VECTOR & DIMENSION
  17. 17. VECTOR & DIMENSION
  18. 18. VECTORS
  19. 19. VECTORS
  20. 20. SIMILARITY CALCULATION
  21. 21. USER SIMILARITY MATRIX
  22. 22. SIMILARITY CALCULATION
  23. 23. SIMILARITY CALCULATION
  24. 24. SIMILARITY CALCULATION EXAMPLE
  25. 25. K-NEAREST-NEIGHBOR
  26. 26. K-NEAREST-NEIGHBOR
  27. 27. NEIGHBORS’ ORDER
  28. 28. REMOVE BOUGHT ITEMS
  29. 29. CALCULATING FINAL SCORE
  30. 30. OTHER SIMILARITY MEASURES More at: http://favi.com.vn/wp-content/uploads/2012/05/pg049_Similarity_Measures_for_Text_Document_Clustering.pdf
  31. 31. Problem ?!
  32. 32. COLLABORATIVE FILTERING PROBLEM • Fail with cold start problem o New User o New Item • Performance o Large Data set o Pre-calculate
  33. 33. PERFORMANCE EXAMPLE • We have 1,000,000 users (customers) • We sell 10,000 items - Total of similarity calculating = 1,000,000 x 1,000,000 = 1,000,000,000,000 - Each similarity calculate need 0.006s (on my MacBook Pro 2.2GHz Core i7, 8G Ram) => We need 1,000,000,000,000 x 0.006 = 6,000,000,000(s) ≈ 70,000 days ≈ 191 years - If store each similarity in 8 bytes, we need = 8,000,000,000,000 bytes ≈ 8,000 GB (on Memory or File)
  34. 34. ITEM-TO-ITEM COLLABORATIVE FILTERING (AMAZON.COM ) Download Paper: http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
  35. 35. ADJUSTMENTS • Hybrid Recommender System • Sale forecast system • Context of User • Type of Item, Action • External (3rd-party) information.
  36. 36. BOOKS Programming Collective Intelligence Toby Segaran Recommender Systems Handbook Many Authors Big Data For Dummies Marcia Kaufman, Fern Halper
  37. 37. OPEN SOURCES
  38. 38. Thank you! CONTACT ME: tuanmaster2002@yahoo.com 0938 916 902 http://bloghoctap.com/

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