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Neural Collaborative Filtering Explanation & Implementation

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Kung-hsiang (Steeve) Huang

Youtube Link: https://youtu.be/O4lk9Lw7lS0

Engineering
1 of 28
Steeve Huang
 1. Propose Neural Collaborative Filtering (NCF) which models the user and item interactions in the latent space effectively with a Neural Network.  2. Show that NCF is a generalization of Matrix Factorization.  3. Demonstrate that NCF outperforms the state-of-the-art models in two real- world datasets.
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 0 0 0 0 0 User 2 0 0 0 0 0 User 3 0 0 0 0 0 User 4 0 0 0 0 0
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 Observed Interaction Unobserved Interaction
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Multiplication
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Multiplication
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
S23 > S12 > S13
S23 > S12 > S13 P1 P2 P3
S23 > S12 > S13 P1 P2 P3 S41 > S43 > S42
S23 > S12 > S13 P1 P2 P3 P4 S41 > S43 > S42
Multiplication Unit Matrix JKx1 L(x) = x
𝑦 𝑢𝑖 = 𝐿 𝑝 𝑢 ⊙ 𝑞𝑖 × 𝐽 𝐾𝑥1 𝑦 𝑢𝑖 = 𝐿 𝑝 𝑢 𝑇 ∙ 𝑞𝑖 𝑦 𝑢𝑖 = 𝑝 𝑢 𝑇 ∙ 𝑞𝑖
Neural Collaborative Filtering Explanation & Implementation
Neural Collaborative Filtering Explanation & Implementation
Neural Collaborative Filtering Explanation & Implementation
Neural Collaborative Filtering Explanation & Implementation
Neural Collaborative Filtering Explanation & Implementation
Neural Collaborative Filtering Explanation & Implementation

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Neural Collaborative Filtering Explanation & Implementation

  • 2.  1. Propose Neural Collaborative Filtering (NCF) which models the user and item interactions in the latent space effectively with a Neural Network.  2. Show that NCF is a generalization of Matrix Factorization.  3. Demonstrate that NCF outperforms the state-of-the-art models in two real- world datasets.
  • 3. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 0 0 0 0 0 User 2 0 0 0 0 0 User 3 0 0 0 0 0 User 4 0 0 0 0 0
  • 4. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0
  • 5. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 Observed Interaction Unobserved Interaction
  • 6. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix
  • 7. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Multiplication
  • 8. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Multiplication
  • 9. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication
  • 10. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
  • 11. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
  • 12. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
  • 13. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
  • 14. Item 1 Item 2 Item 3 Item 4 Item 5 User 1 1 0 0 1 0 User 2 0 0 0 0 1 User 3 0 0 0 1 0 User 4 0 0 1 0 0 0 1.3 -0.6 0 0 0.9 -0.8 0 0 0 -0.8 0 -0.6 0.5 0 0 0.8 0 ≈ X Item MatrixUser MatrixUtility Matrix 0.7 0 0 1.1 0 0 0 0.5 0 0.4 0.5 0 0 0.7 0 0 0 0.6 0 0.5 Mean Squared Error Multiplication k
  • 15.
  • 16. S23 > S12 > S13
  • 17. S23 > S12 > S13 P1 P2 P3
  • 18. S23 > S12 > S13 P1 P2 P3 S41 > S43 > S42
  • 19. S23 > S12 > S13 P1 P2 P3 P4 S41 > S43 > S42
  • 20.
  • 22. 𝑦 𝑢𝑖 = 𝐿 𝑝 𝑢 ⊙ 𝑞𝑖 × 𝐽 𝐾𝑥1 𝑦 𝑢𝑖 = 𝐿 𝑝 𝑢 𝑇 ∙ 𝑞𝑖 𝑦 𝑢𝑖 = 𝑝 𝑢 𝑇 ∙ 𝑞𝑖