Machine learning at Criteo - Paris Datageeks
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Criteo is an advertising company that displays ads on websites for its partners. It uses machine learning for click prediction models and product recommendations. Criteo has a large infrastructure across multiple data centers and handles over 30 billion HTTP requests per day. The presentation discusses the challenges of building machine learning models at Criteo's scale for real-time bidding and recommendations, such as dealing with many variables, changing products, and different timeframes.
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Machine learning at Criteo - Paris Datageeks
- 1. Shallow learning @ Criteo Nicolas Le Roux Scientific Program Manager – R&D
- 2. I N T R O D U C T I O N Copyright © 2013 Criteo
- 3. WHAT DOES CRITEO DO? 3 • We buy advertising space on websites • We display ads for our partners • We get paid if the user clicks on the ad Copyright © 2013 Criteo
- 4. HOW DO WE BUY ADVERTISING SPACE? 4 • Real-time bidding (RTB): it is an auction • Second-price • Optimal strategy: bid the expected gain • Expected gain = CPC * CTR Copyright © 2013 Criteo
- 5. WHAT TO DO ONCE WE WIN THE DISPLAY? 5 • Choose the best products • Choose the color, the font, the layout… • Generate the banner Copyright © 2013 Criteo
- 6. WHERE IS THE ML AT CRITEO? 6 • Click prediction model • Product recommendation • Testing, … Copyright © 2013 Criteo
- 7. WHERE IS THE ML AT CRITEO? 7 • Click prediction model • Product recommendation • Testing, … Copyright © 2013 Criteo
- 8. INFRASTRUCTURE AT CRITEO 8 • 5 data centers on 3 continents • More than 3 500 servers • More than 30B HTTP requests per day • More than 2B banners displayed per day • Peak traffic: 500K HTTP requests per second Copyright © 2013 Criteo
- 9. C L I C K P R E D I C T I O N M O D E L Copyright © 2013 Criteo
- 10. CTR PREDICTION MODEL 10Copyright © 2013 Criteo
- 11. CTR PREDICTION MODEL 11Copyright © 2013 Criteo
- 12. CTR PREDICTION MODEL 12Copyright © 2013 Criteo
- 13. CTR PREDICTION MODEL 13Copyright © 2013 Criteo
- 14. CTR PREDICTION MODEL 14Copyright © 2013 Criteo
- 15. 15-SECOND ML CRASH COURSE 15Copyright © 2013 Criteo
- 16. NEW HIRE REACTION 16Copyright © 2013 Criteo
- 17. NEW HIRE REACTION 17Copyright © 2013 Criteo
- 18. NEW HIRE REACTION 18Copyright © 2013 Criteo
- 19. NEW HIRE REACTION 19Copyright © 2013 Criteo
- 20. 20Copyright © 2013 Criteo
- 21. 21Copyright © 2013 Criteo
- 22. 22 • Merchant ID is too raw • We can learn fancy features for each merchant: demographics, size, ... • Hard to not penalize large merchants Copyright © 2013 Criteo
- 23. OPTIMIZING THE MODEL 23Copyright © 2013 Criteo
- 24. DEALING WITH MANY VARIABLES 24Copyright © 2013 Criteo
- 25. DEALING WITH MANY VARIABLES 25Copyright © 2013 Criteo
- 26. OTHER CHALLENGES 26 • How long do you wait for sales after a click? • How much in the past do you go to learn the model? • Do you value more long term or short term variables? • How to choose the best variables? Copyright © 2013 Criteo
- 27. P R O D U C T R E C O M M E N D A T I O N Copyright © 2013 Criteo
- 28. THE CHALLENGES OF PRODUCT RECOMMENDATION 28Copyright © 2013 Criteo
- 29. THE CHALLENGES OF PRODUCT RECOMMENDATION 29Copyright © 2013 Criteo
- 30. NEW HIRE REACTION 30 • « You should use [his PhD topic], it’s awesome! » Copyright © 2013 Criteo
- 31. NEW HIRE REACTION 31 • « You should use [his PhD topic], it’s awesome! » • « It just takes a week to train. » Copyright © 2013 Criteo
- 32. NEW HIRE REACTION 32 • « You should use [his PhD topic], it’s awesome! » • « It just takes a week to train. » • « In real-time, you just have to score every product. » Copyright © 2013 Criteo
- 33. MAJOR HURDLES 33 • Products come and go • There might be a sale (Black Friday) • The « panties syndrome » • Complementary vs. similar products Copyright © 2013 Criteo
- 34. OTHER CHALLENGES 34 • Multiple products in a banner • Interaction between products and layout • Different timeframes for different products Copyright © 2013 Criteo
- 35. A FIRST RECIPE FOR SUCCESS 35 • There are many sources of success/failure • It is often suboptimal to focus on one • The first step to address each source is often manual Copyright © 2013 Criteo
- 36. CONCLUSION 36 • Prediction is at the core of our business • Huge engineering constraints • Different bottlenecks than in academia • Build from the ground up, not the other way around Copyright © 2013 Criteo
- 37. Nicolas: n.leroux@criteo.com Lisa: l.sarny@criteo.com Clem: c.daines@criteo.com QUESTIONS ?
Notas do Editor
- We’re really happy to have you all here. On a completely unrelated note, we’re hiring. If you’d like to know more, you’re welcome to chat with the two lovely ladies: Lisa and Clementine.Avant de commencer, je voulaisvousremercier d’être venus, on estravis de vousavoirici. A ce propos, je tenais à signaler qu’onrecrute. Si vousêtesintéressé, n’hésitez pas à discuter avec les deuxcharmantespersonnes qui sontlà pour ça: Lisa et Clementine.
- An example of phi is: rather than pick the time of day, split between “morning, work hours” and “evening”.
- I’m using the masculine form because Criteo is unfortunately slightly gender-biased.
- Every time your bid is slower, you lose money: timeout, RTB platform does not want to deal with you anymore, ...This is not image recognition. Keep it simple: a tree, a linear model, ...
- SGD is not advantageous because, when you have 10 times more data, you don’t have that much more information
- SGD is not advantageous because, when you have 10 times more data, you don’t have that much more information
- SGD is not advantageous because, when you have 10 times more data, you don’t have that much more information
- Go in the past: business team started a new campaign with high CPC which flooded your dataset.Long term vs short term: expensive computations vs cheap onesChoose the best variables: each variable adds latency.Conclusion: there are many more questions than “What is the best f I can think of?”
- I’m using the masculine form because Criteo is unfortunately slightly gender-biased.
- I’m using the masculine form because Criteo is unfortunately slightly gender-biased.
- SVD, NNMF, LDA, Bayesian Matrix Factorization, Sparse Coding, ...
- Panties: if you create a startup, display panties.
- There are instant buys and other products which need a bigger thought. DVD vs. car
- There are instant buys and other products which need a bigger thought. DVD vs. car
- It is not about “How fast can you go with a complicated model?” but “How complex can you go within the resource budget?”