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Deep Learning forRecommender Systems
Justin Basilico & Yves RaimondMarch 28, 2018GPU Technology Conference
@JustinBasilico @moustaki
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
Deep learning for recommendations: a first try
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UsersIte
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Traditional Recommendation Setup
U≈RV
A Matrix Factorization view
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A Feed-Forward Network view
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U
A (deeper) feed-forward view
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Mean squared loss?
GPU vs. CPU●
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What’s going on?●
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Conclusion?●
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Breaking the ‘traditional’ recsys setup
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Alternative data
Content-based side information●
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Metadata-based side information●
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Alternative models
Restricted Boltzmann Machines●
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Auto-encoders●●
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(*)2Vec●
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prod2vec (Skip-gram)
user2vec(Continuous Bag of Words)
Alternative framings
Sequence prediction●
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Contextual sequence prediction●
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Contextual sequence data
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Context ActionSequenceper user
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Tim
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Time-sensitive sequence prediction●
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Other framings●
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Conclusion
Takeaways●
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More Resources●●●●●●
Thank you. @JustinBasilico @moustaki
Justin Basilico & Yves Raimond
Yes, we’re hiring...
