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DEEPER THINGS Aish Fenton @aishfenton Sudeep Das @datamusing
Popular On Netflix
● 1999-2005: DVD ● Netflix Prize (2006-2009): ○ > 10% improvement, win $1,000,000 ● Top performing model(s) ended up being...
USERSITEMS 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 Traditional Recommendation Setup
U≈R V Matrix Factorization
U≈R V Matrix Factorization
GOING DEEPER
If MF were a Neural Network… V U
Mean Squared Loss? Now we can go deeper V U
The naive approach … V U The knee-jerk reaction would be to add multiple fully connected layers here. Does that work? Loss...
Loss function … isn’t always the best! V U
But NNs open up many other possibilities ... Softmax BOW -average / N-gram / Sequence Feed-forward/ LSTM Input interaction...
Adding time and context Softmax Sequence LSTM Input interactions (X) p(Y) 2018-12-2319:32:10 2018-12-2412:05:53 2019-01-02...
Offline ranking metrics
Loss function ? Adding heterogeneous side information ... V U User Metadata: e.g. Country, Preferred Language Item Metadat...
Rich hierarchical side information cannot be easily embedded into Euclidean space Politically Incorrect Stand Up Stand Up ...
STILL DEEPER
Riddle: What’s so vast it can’t fit in a deep neural network?
Bright Birdbox Sandra Bullock Mr and Mrs. Smith Will Smith
EXPONENTIALGROWTH
We need to leave Euclidean space and go into Hyperbolic Space
EXPONENTIAL GROWTH WITH R NEG CURVATURE
V Uu v
u v dist(u,v) gradient of operations V U
Differential geometry provides the answer!
EXPONENTIALGROWTH
QUESTIONS ?
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search
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Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search

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In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.

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Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search

  1. 1. DEEPER THINGS Aish Fenton @aishfenton Sudeep Das @datamusing
  2. 2. Popular On Netflix
  3. 3. ● 1999-2005: DVD ● Netflix Prize (2006-2009): ○ > 10% improvement, win $1,000,000 ● Top performing model(s) ended up being Matrix Factorization (SVD++, Koren, et al) ● We’ve moved on, but MF is still a much-used foundational method Netflix,Recommendation Systems, ML
  4. 4. USERSITEMS 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 Traditional Recommendation Setup
  5. 5. U≈R V Matrix Factorization
  6. 6. U≈R V Matrix Factorization
  7. 7. GOING DEEPER
  8. 8. If MF were a Neural Network… V U
  9. 9. Mean Squared Loss? Now we can go deeper V U
  10. 10. The naive approach … V U The knee-jerk reaction would be to add multiple fully connected layers here. Does that work? Loss function
  11. 11. Loss function … isn’t always the best! V U
  12. 12. But NNs open up many other possibilities ... Softmax BOW -average / N-gram / Sequence Feed-forward/ LSTM Input interactions (X) p(Y) V
  13. 13. Adding time and context Softmax Sequence LSTM Input interactions (X) p(Y) 2018-12-2319:32:10 2018-12-2412:05:53 2019-01-0215:40:22 V
  14. 14. Offline ranking metrics
  15. 15. Loss function ? Adding heterogeneous side information ... V U User Metadata: e.g. Country, Preferred Language Item Metadata: e.g. Country of origin, synopsis embedding, boxart embedding
  16. 16. Rich hierarchical side information cannot be easily embedded into Euclidean space Politically Incorrect Stand Up Stand Up Comedies Romantic Comedies Raunchy Stand Up Feel Good Romantic Comedies Romantic Comedies based on Books Women Who Make Us Laugh Alltagging metadata Documentaries
  17. 17. STILL DEEPER
  18. 18. Riddle: What’s so vast it can’t fit in a deep neural network?
  19. 19. Bright Birdbox Sandra Bullock Mr and Mrs. Smith Will Smith
  20. 20. EXPONENTIALGROWTH
  21. 21. We need to leave Euclidean space and go into Hyperbolic Space
  22. 22. EXPONENTIAL GROWTH WITH R NEG CURVATURE
  23. 23. V Uu v
  24. 24. u v dist(u,v) gradient of operations V U
  25. 25. Differential geometry provides the answer!
  26. 26. EXPONENTIALGROWTH
  27. 27. QUESTIONS ?

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