The document outlines the process of building a recommender system, discussing various types such as collaborative filtering, content-based filtering, and hybrid methods. It highlights the challenges faced, including the cold start problem and performance issues with large datasets. The presentation also includes practical examples and adjustments for improving recommendations and invites further contact for more information.

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/