Menno van der Sman, Lead Engineer of Wakoopa presents at the AWS Start-Up Event - Amsterdam about their use of Amazon EC2 and S3 for their recommendations engine.
7. How to get started?
Research Mathemagicians
Amazon, Netflix etc
Peter Tegelaar & Coen Stevens
Ludwig created
recommender system in ruby running on EC2
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
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
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
= 2.8
0.6 + 0.8 + 0.4 + 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