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Copyright © 2015 Criteo
Product Recommendation Beyond Collaborative
Filtering - welcome to the Twilight Zone!
Olivier Koch...
Copyright © 2015 Criteo
Outline
• What we do
• Lessons learned from building collaborative filtering at scale
• Now what?
Copyright © 2015 Criteo
What we do
• Buy ad space on publishers’ websites.
• Build banners showing products that users wil...
3 billion ads/day
5 billion products
100 ms
15 data centers
24 000 servers
2000-node hadoop cluster
Copyright © 2015 Criteo
Copyright © 2015 Criteo
Machine learning is at the core of Criteo
• How much should we bid?
• What look & feel should we u...
Copyright © 2015 Criteo
Recommending products from user timelines
Clicks / Sales / Views / Baskets events (on advertisers’...
Bob saw orange shoes
Some candidate products
Historical
Similar
Complementary
Most viewed
Lessons learned from building
a CF system at scale
Start simple (counters)
Expect a long tail
Randomize
Yes, your Spark jobs will break
Log everything (and more)
Revisit you...
CF is great…
intuitive
simple to implement
scales relatively well
captures many implicit signals via wisdom of the crowd
But CF has limitations too…
does not scale that well actually (quadratic in user timelines)
does not capture temporal sign...
Deep learning to the rescue
Fusing image, text and CF (Content2vec)
Specializing Joint Representations for the task of Pro...
Deep learning to the rescue
Hierarchical recurrent neural networks
Personalizing Session-based Recommendations with Hierar...
Deep learning to the rescue
Can we build neural network architectures that will make our recommendations
more relevant?
Ca...
Attribution and incrementality
The true objective of recommendation is to predict and show ads that cause new
(incremental...
Copyright © 2014 Criteo
Join us!
We are reverse-engineering the brain
of 1B+ shoppers worldwide!
http://labs.criteo.com/rd...
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2017 09-20-criteo-recsys-london

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RecSys London Meetup Criteo Presentation - fusing collaborative filtering and deep learning at scale for billions of users and products

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2017 09-20-criteo-recsys-london

  1. 1. Copyright © 2015 Criteo Product Recommendation Beyond Collaborative Filtering - welcome to the Twilight Zone! Olivier Koch, Machine Learning Lead o.koch@criteo.com @olivkoch RecSys Meetup London - Sept 20, 2017
  2. 2. Copyright © 2015 Criteo Outline • What we do • Lessons learned from building collaborative filtering at scale • Now what?
  3. 3. Copyright © 2015 Criteo What we do • Buy ad space on publishers’ websites. • Build banners showing products that users will like / want to buy. • Get paid if users click / buy the product.
  4. 4. 3 billion ads/day 5 billion products 100 ms
  5. 5. 15 data centers 24 000 servers 2000-node hadoop cluster
  6. 6. Copyright © 2015 Criteo
  7. 7. Copyright © 2015 Criteo Machine learning is at the core of Criteo • How much should we bid? • What look & feel should we use? • Which products should we recommend?
  8. 8. Copyright © 2015 Criteo Recommending products from user timelines Clicks / Sales / Views / Baskets events (on advertisers’ websites) Click / no-click / attributed sales events (on publishers’ websites) User = ? … for billions of users and products
  9. 9. Bob saw orange shoes Some candidate products Historical Similar Complementary Most viewed
  10. 10. Lessons learned from building a CF system at scale
  11. 11. Start simple (counters) Expect a long tail Randomize Yes, your Spark jobs will break Log everything (and more) Revisit your evaluation metrics Revisit your features Check your attribution model Beating CF is really hard
  12. 12. CF is great… intuitive simple to implement scales relatively well captures many implicit signals via wisdom of the crowd
  13. 13. But CF has limitations too… does not scale that well actually (quadratic in user timelines) does not capture temporal signals does not solve cold start does not address exploration in the long tail
  14. 14. Deep learning to the rescue Fusing image, text and CF (Content2vec) Specializing Joint Representations for the task of Product Recommendation, Thomas Nedelec, Elena Smirnova, Flavian Vasile, RecSys 2017 DL Workshop, arXiv:1706.07625 Contextual RNNs Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks, Elena Smirnova, Flavian Vasile, RecSys 2017 DL Workshop, arXiv:1706.07684
  15. 15. Deep learning to the rescue Hierarchical recurrent neural networks Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks by Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi, RecSys 2017 Compressed embeddings Getting Deep Recommenders Fit: Bloom Embeddings for Sparse Binary Input/Output Networks by Joan Serrà and Alexandros Karatzoglou, RecSys 2017 3D Convolutional Networks for Session-based Recommendation with Content Features, Trinh Xuan Tuan and Tu Minh Phuong, RecSys 2017
  16. 16. Deep learning to the rescue Can we build neural network architectures that will make our recommendations more relevant? Can we leverage temporal information and product metadata? At scale.
  17. 17. Attribution and incrementality The true objective of recommendation is to predict and show ads that cause new (incremental) sales Large-scale Validation of Counterfactual Learning Methods: A Test-Bed. Damien Lefortier, Xiaotao Gu, Adith Swaminathan, Thorsten Joachims and Maarten de Rijke, arXiv:1612.00367, NIPS What If 2016 http://research.criteo.com/dataset-release-evaluation-counterfactual-algorithms/
  18. 18. Copyright © 2014 Criteo Join us! We are reverse-engineering the brain of 1B+ shoppers worldwide! http://labs.criteo.com/rd-jobs/

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