Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA. Abstract: In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.

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. 0 1 0 1 0
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0 0 0 0 1
UsersItems
Traditional Recommendation Setup

5. U≈R
V
A Matrix Factorization view

6. U
A Feed-Forward Network view
V

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

8. A quick & dirty experiment
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9. GPU vs. CPU
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12. What’s going on?
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13. Conclusion?
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14. Breaking the ‘traditional’ recsys setup
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15. Alternative data

16. Content-based side information
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17. Metadata-based side information
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18. YouTube Recommendations
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19. Alternative models

20. Restricted Boltzmann Machines
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21. Auto-encoders
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22. (*)2Vec
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prod2vec
(Skip-gram)
user2vec
(Continuous Bag of Words)

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

24. Alternative framings

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

28. Time-sensitive sequence prediction
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31. Other framings
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32. Conclusion

33. Takeaways
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34. More Resources
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35. Thank you.
@JustinBasilico @moustaki
Justin Basilico & Yves Raimond
Yes, we’re hiring...