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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 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
0 1 0 1 0
0 0 1 1 0
1 0 0 1 1
0 1 0 0 0
0 0 0 0 1
UsersItems
Traditional Recommendation Setup
U≈R
V
A Matrix Factorization view
U
A Feed-Forward Network view
V
U
A (deeper) feed-forward view
V
Mean
squared loss?
A quick & dirty experiment
●
○
○
●
○
■
■
○
■
■
■
■
■
●
GPU vs. CPU
●
●
●
What’s going on?
●
●
●
●
Conclusion?
●
●
Breaking the ‘traditional’ recsys setup
●
●
●
Alternative data
Content-based side information
●
●
●
Metadata-based side information
●
○
●
○
●
●
YouTube Recommendations
●
●
Alternative models
Restricted Boltzmann Machines
●
●
●
Auto-encoders
●
●
○
●
●
●
(*)2Vec
●
●
●
prod2vec
(Skip-gram)
user2vec
(Continuous Bag of Words)
Wide + Deep models
●
●
[Cheng et. al., 2016]
Alternative framings
Sequence prediction
●
○
○
●
○
○
●
Contextual sequence prediction
●
●
●
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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
?
Time
Time-sensitive sequence prediction
●
○
●
○
■
●
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■
○
Other framings
●
○
●
○
●
Conclusion
Takeaways
●
●
●
●
More Resources
●
●
●
●
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●
Thank you.
@JustinBasilico @moustaki
Justin Basilico & Yves Raimond
Yes, we’re hiring...

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