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

1. Recommender
System
How to build a

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. Introduction
Collaborative Filtering
Question & Answer
AGENDA

4. 1. Introduction

5. APPLICATIONS
• Personalized recommendation
• Social recommendation
• Item recommendation
• Combination of 3 approaches above

6. AMAZON.COM | BOOKS

7. PLAY.GOOGLE.COM | APPS

8. SKILLSHARE.COM | CLASSES

9. PROCESS DIAGRAM
Preprocessing Data Analysis Adjustment
INPUT OUTPUT

10. TYPE OF RECOMMENDER SYSTEM
• Collaborative filtering
• Content-based filtering
• Hybrid

11. 2. Collaborative
Filtering

12. USER & ITEM

13. ORDER DATA

14. ORDER DATA (cont.)

15. ORDER DATA (cont.)

16. VECTOR & DIMENSION

17. VECTOR & DIMENSION

18. VECTORS

19. VECTORS

20. SIMILARITY CALCULATION

21. USER SIMILARITY MATRIX

22. SIMILARITY CALCULATION

23. SIMILARITY CALCULATION

24. SIMILARITY CALCULATION EXAMPLE

25. K-NEAREST-NEIGHBOR

26. K-NEAREST-NEIGHBOR

27. NEIGHBORS’ ORDER

28. REMOVE BOUGHT ITEMS

29. CALCULATING FINAL SCORE

30. OTHER SIMILARITY MEASURES
More at: http://favi.com.vn/wp-content/uploads/2012/05/pg049_Similarity_Measures_for_Text_Document_Clustering.pdf

31. Problem ?!

32. COLLABORATIVE FILTERING PROBLEM
• Fail with cold start problem
o New User
o New Item
• Performance
o Large Data set
o Pre-calculate

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. ITEM-TO-ITEM COLLABORATIVE FILTERING
(AMAZON.COM )
Download Paper: http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf

35. ADJUSTMENTS
• Hybrid Recommender System
• Sale forecast system
• Context of User
• Type of Item, Action
• External (3rd-party) information.

36. BOOKS
Programming Collective
Intelligence
Toby Segaran
Recommender Systems
Handbook
Many Authors
Big Data For Dummies
Marcia Kaufman, Fern Halper

37. OPEN SOURCES

38. Thank you!
CONTACT ME:
tuanmaster2002@yahoo.com
0938 916 902
http://bloghoctap.com/