The slides of the talk I gave at AWS start-up event in Amsterdam.

2. Menno van der Sman
Lead Developer
Coen Stevens
Recommendation Engineer

3. Mission:
Discover software & games

4. Updates

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
Cosine 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
= 1.5
0.6 + 0.8 + 0.4 + 0.4 + 0.3 + 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. 1 evening, 3 speakers, 100 developers
Pre-register on www.recked.org