The document discusses the use of deep learning for recommender systems, emphasizing its potential to enhance personalization in recommendations, which significantly impacts customer satisfaction and revenue. It explores traditional and alternative recommendation setups, including various models and approaches such as matrix factorization and deep learning techniques. Key takeaways include the importance of contextual data and diverse modeling strategies to improve recommendation accuracy.

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

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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
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2017-12-24 12:05:53
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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
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