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Deep Learning for Recommender Systems Justin Basilico & Yves - PowerPoint PPT Presentation

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Deep Learning for Recommender Systems Justin Basilico & Yves Raimond March 28, 2018 GPU Technology Conference @JustinBasilico @moustaki The value of recommendations A few seconds to find something great to watch Can only show a

  1. Deep Learning for Recommender Systems Justin Basilico & Yves Raimond March 28, 2018 GPU Technology Conference @JustinBasilico @moustaki

  2. The value of recommendations A few seconds to find something ● great to watch… Can only show a few titles ● Enjoyment directly impacts ● customer satisfaction Generates over $1B per year of ● Netflix revenue How? Personalize everything ●

  3. Deep learning for recommendations: a first try

  4. Traditional Recommendation Setup Users 1 1 0 0 0 1 1 0 0 0 Items 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0

  5. A Matrix Factorization view V ≈ R U

  6. A Feed-Forward Network view U V

  7. A (deeper) feed-forward view U ? Mean squared loss V

  8. A quick & dirty experiment ● ○ ○ ● ○ ■ ■ ○ ■ ■ ■ ■ ■ ●

  9. GPU vs. CPU ● ● ●

  12. What’s going on? ● ● ● ●

  13. Conclusion? ● ●

  14. Breaking the ‘traditional’ recsys setup ● ● ●

  15. Alternative data

  16. Content-based side information ● ● ●

  17. Metadata-based side information ● ○ ● ○ ● ●

  18. YouTube Recommendations ● ●

  19. Alternative models

  20. Restricted Boltzmann Machines ● ● ●

  21. Auto-encoders ● ● ○ ● ● ●

  22. prod2vec (Skip-gram) (*)2Vec ● ● ● user2vec (Continuous Bag of Words)

  23. Wide + Deep models ● ● [Cheng et. al., 2016]

  24. Alternative framings

  25. Sequence prediction ● ○ ○ ● ○ ○ ●

  26. Contextual sequence prediction ● ● ● ●

  27. Contextual sequence data Sequence Context Action per user 2017-12-10 15:40:22 2017-12-23 19:32:10 2017-12-24 12:05:53 Time 2017-12-27 22:40:22 2017-12-29 19:39:36 ? 2017-12-30 20:42:13

  28. Time-sensitive sequence prediction ● ○ ● ○ ■ ● ● ■ ○

  31. Other framings ● ○ ● ○ ●

  32. Conclusion

  33. Takeaways ● ● ● ●

  34. More Resources ● ● ● ● ● ●

  35. Thank you. Justin Basilico & Yves Raimond @JustinBasilico @moustaki Yes, we’re hiring...

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