Timeline Post Recommender System

by Jihong Lee@LINE TECHPULSE 2019 https://techpulse.line.me/

Transcript

2. > Jihong Lee / Data Science Dev LINE Timeline Post Recommender System
3. Agenda > What is a Recommender System? > Problems, Solutions, and Lessons Learned • User Embeddings • Feedback Loop • Importance of Evaluation • Model Architecture • Increasing Post Pool Size
4. What Is a Recommender System?
5. What Is a Recommender System?
6. What Is a Recommender System?
7. LINE Timeline
8. Background Knowledge > Collaborative filtering and content-based filtering > Two big approaches to Recommender Systems
9. Collaborative Filtering Movie A Movie B Movie C Movie D 5 4 5 2 3 4 4 2 1 1 Ratings 5 - Excellent 4 - Good 3 - Average 2 - Not Bad 1 - Bad
10. Collaborative Filtering Movie A Movie B Movie C Movie D 5 4 5 2 3 4 4 2 1 1 Ratings 5 - Excellent 4 - Good 3 - Average 2 - Not Bad 1 - Bad
11. Collaborative Filtering Movie A Movie B Movie C Movie D 5 4 5 2 3 4 4 2 1 1 Ratings 5 - Excellent 4 - Good 3 - Average 2 - Not Bad 1 - Bad
12. Collaborative Filtering Movie A Movie B Movie C Movie D 5 4 5 2 3 4 4 2 1 1 Ratings 5 - Excellent 4 - Good 3 - Average 2 - Not Bad 1 - Bad Recommend!
13. Problem Description > Given: A user and a post with context > GOAL: Predict the probability that the user will click the post
14. User Interactions Model Deployment Model Training Preprocess Data Model Training Pipeline Raw Data
15. Model Training Pipeline User Interactions Model Deployment Model Training Preprocess Data Raw Data
16. Model Training Pipeline User Interactions Model Deployment Model Training Raw Data Preprocess Data
17. Raw Data User View History Log User Post Author Time User Click History Log User Post Author Time Join!
18. Raw Data Labeled Data User Post Author Time Label 1 0 1 1 1 - Positive Label (Clicked) 0 - Negative Label (Not Clicked)
19. Model Training Pipeline User Interactions Model Deployment Model Training Raw Data Preprocess Data
20. Model Training Pipeline User Interactions Model Deployment Model Training Raw Data Preprocess Data
21. Model Training Pipeline User Interactions Model Deployment Model Training Raw Data Preprocess Data
22. Model Training Pipeline Simple! User Interactions Model Deployment Model Training Raw Data Preprocess Data User Interactions Model Deployment Model Training Raw Data Preprocess Data
23. Model Training Pipeline User Interactions Model Deployment Model Training Raw Data Preprocess Data
24. Recommender System 101: Embeddings Item 1 3 0 2 0 … Item 2 2 1 2 4 … Item 3 3 4 1 0 … features dimensionality too high! (# of columns) no meaning of categories
25. Recommender System 101: Embeddings Item 1 3 0 2 0 … Item 2 2 1 2 4 … Item 3 3 4 1 0 … features dimensionality too high! (# of columns) Item 1 [ -0.242 0.218 0.848 -0.887 … ] Item 1 [ -0.242 0.218 0.848 -0.887 … ] Item 1 [ -0.242 0.218 0.848 -0.887 … ] embedding representation no meaning of categories reduced dimensionality has category meanings
26. Problems & Solutions > User Embeddings
27. Creating Embeddings Post 1 Post 2 Post 3 Post 4 0 1 1 1 0 1 0 1 1 Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] User Embedding Vectors [ 0.324 -0.192 -0.453 0.004 … ] [ -0.187 0.394 -0.225 0.022 … ] [ 0.177 0.718 -0.239 -0.422 … ] [ -0.725 0.090 -0.353 0.228 … ] … [ … ]
28. Creating Embeddings Post 1 Post 2 Post 3 Post 4 0 1 1 1 0 1 0 1 1 Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] User Embedding Vectors [ 0.324 -0.192 -0.453 0.004 … ] [ -0.187 0.394 -0.225 0.022 … ] [ 0.177 0.718 -0.239 -0.422 … ] [ -0.725 0.090 -0.353 0.228 … ] … [ … ] Extremely Sparse!! Density < 0.0001%
29. Mitigate the Issue Post 1 Post 2 Post 3 Post 4 Post 1 3 0 2 0 Post 2 1 1 3 2 Post 3 1 0 2 1 Post 4 0 1 4 1 Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] User Embedding Vectors [ linear combination of user history ] [ linear combination of user history ] [ linear combination of user history ] [ linear combination of user history ] … [ … ] Worked Much Better!
30. Lesson Learned > Different algorithms are suitable for different types of data > Must understand the nature of your data!
31. Problems & Solutions > Feedback Loop
32. Feedback Loop User Interactions Model Deployment Model Training Raw Data Preprocess Data User Interactions Model Deployment Model Training Raw Data Preprocess Data
33. Feedback Loop Problematic! User Interactions Model Deployment Model Training Raw Data Preprocess Data User Interactions Model Deployment Model Training Raw Data Preprocess Data
34. Feedback Loop train recommendations for user user interacts Caused by Model 0 Training Data Is Biased!! Model 0 Model 1
35. User Interactions Model Deployment Model Training Preprocess Data Feedback Loop User Interactions Model Deployment Model Training Raw Data Preprocess Data Raw Data Caused by Friends’ Shares User Interactions Model Deployment Model Training Raw Data Preprocess Data Not Used in Training!
36. Problems & Solutions > Importance of Evaluation
37. AUROC (Area Under ROC Curve) True Positive Rate False Positive Rate True Positive Rate: Proportion of Correctly Classified Positive Labels False Positive Rate: Proportion of Negative Labels Incorrectly Classified as Positive : Trained Classifier : Random Classifier
38. AUROC (Area Under ROC Curve) True Positive Rate False Positive Rate True Positive Rate: Proportion of Correctly Classified Positive Labels False Positive Rate: Proportion of Negative Labels Incorrectly Classified as Positive : Trained Classifier : Random Classifier
39. Global AUROC User Post Pr(Click) A 0.992 B 0.981 A 0.977 C 0.964 B 0.951 A 0.924 C 0.918 A 0.908 C 0.905 C 0.900 B 0.898 B 0.891 … … … Label 1 0 1 1 0 0 1 1 0 1 1 0 … Calculate AUROC
40. Global AUROC User Post Pr(Click) A 0.992 B 0.981 A 0.977 C 0.964 B 0.951 A 0.924 C 0.918 A 0.908 C 0.905 C 0.900 B 0.898 B 0.891 … … … Label 1 0 1 1 0 0 1 1 0 1 1 0 … Calculate AUROC
41. Average AUROC per User Label 1 0 1 … User Post Pr(Click) A 0.992 B 0.981 C 0.977 … … … User Post Pr(Click) A 0.990 C 0.985 D 0.972 … … … User Post Pr(Click) B 0.991 D 0.970 C 0.967 … … … Label 1 0 1 … Label 1 0 1 … Calculate AUROC for each user then average
42. Problems & Solutions > Model Architecture
43. Problems With a Sole Ranking Model Computationally expensive Concentrated post distribution
44. Problems With a Sole Ranking Model Concentrated post distribution Computationally expensive
45. Problems With a Sole Ranking Model Computationally expensive Concentrated post distribution
46. Problems With a Sole Ranking Model Computationally expensive Concentrated post distribution
47. Pareto Optimality Preference Criterion A Preference Criterion B a state of maximum efficiency in the allocation of resources Pareto frontier any point on the Pareto frontier is Pareto optimal
48. Pareto Optimality Preference Criterion A Preference Criterion B a state of maximum efficiency in the allocation of resources Pareto frontier any point on the Pareto frontier is Pareto optimal
49. Current State Personalization KPI Inside Pareto Frontier Converging at KPI’s Local Maxima A B
50. In Search of Pareto Frontier Personalization KPI Need a change in model architecture
51. Candidate Generation and Ranking Raw Data User Interactions Model Deployment Candidate Generation Ranking Preprocess Data Preprocess Data
52. Candidate Generation and Ranking Raw Data User Interactions Model Deployment Candidate Generation Ranking Preprocess Data Preprocess Data
53. Candidate Generation Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] train post embeddings = Post 1 Post 2 Post 3 Post 4 Post 1 3 0 2 0 Post 2 1 1 3 2 Post 3 1 0 2 1 Post 4 0 1 4 1 co-occurrence matrix Candidate Generation Training
54. Candidate Generation Candidate Generation Inference user ID = interaction history Item Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] linear combination of history User ID [ -0.242 0.218 0.848 -0.887 … ] user vector nearest neighbor search candidates Candidates Post 1 Post 2 Post 3 Post 4 …
55. Inference Pipeline User ID Query Candidate Generation Ranking Recommendations
56. Problems & Solutions > Increasing Post Pool
57. Aligning Embeddings Trained in Batches Candidate Generation Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] Candidate Generation Post Embedding Vectors Post 3 [ -0.242 0.218 0.848 -0.887 … ] Post 4 [ 0.581 -0.859 0.006 -0.598 … ] Post 5 [ 0.344 -0.834 -0.651 0.524 … ] Post 6 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] Candidate Generation Post Embedding Vectors Post 5 [ -0.242 0.218 0.848 -0.887 … ] Post 6 [ 0.581 -0.859 0.006 -0.598 … ] Post 7 [ 0.344 -0.834 -0.651 0.524 … ] Post 8 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] t=0 t=1 t=2 Each batch has a different post pool
58. Aligning Embeddings Trained in Batches Candidate Generation Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] Candidate Generation Post Embedding Vectors Post 3 [ -0.242 0.218 0.848 -0.887 … ] Post 4 [ 0.581 -0.859 0.006 -0.598 … ] Post 5 [ 0.344 -0.834 -0.651 0.524 … ] Post 6 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] Candidate Generation Post Embedding Vectors Post 5 [ -0.242 0.218 0.848 -0.887 … ] Post 6 [ 0.581 -0.859 0.006 -0.598 … ] Post 7 [ 0.344 -0.834 -0.651 0.524 … ] Post 8 [ 0.255 0.963 -0.127 -0.959 … ] … [ … ] t=0 t=1 t=2 We want everything Need to align!
59. Orthogonal Procrustes Problem find orthogonal matrix W same size Given A B , such that ∥BW − A∥2 F is minimized
60. Orthogonal Procrustes Problem A B
61. Orthogonal Procrustes Problem A B 1-to-1 correspondence of points find rotation and/or reflection matrix that maps B into A
62. Orthogonal Procrustes Problem 1-to-1 correspondence of points A B find rotation and/or reflection matrix that maps B into A
63. Orthogonal Procrustes Problem 1-to-1 correspondence of points A B find rotation and/or reflection matrix that maps B into A
64. Orthogonal Procrustes Problem Aligned A & B! How can we use this method? B A
65. Orthogonal Procrustes Problem Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] Post 5 [ 0.239 -0.646 0.002 -0.702 … ] Post 6 [ -0.612 -0.408 0.052 0.064 … ] Post 7 [ 0.139 0.118 -0.142 -0.157 … ] … [ … ] Post Embedding Vectors Post 3 [ -0.242 0.218 0.848 -0.887 … ] Post 4 [ 0.581 -0.859 0.006 -0.598 … ] Post 5 [ 0.344 -0.834 -0.651 0.524 … ] Post 6 [ 0.255 0.963 -0.127 -0.959 … ] Post 7 [ -0.299 0.808 0.677 -0.604 … ] Post 8 [ 0.992 -0.795 0.062 -0.490 … ] Post 9 [ 0.855 0.622 -0.793 -0.329 … ] … [ … ] t=0 t=1 A B 1-to-1 correspondence of points find orthogonal matrix W that maps B into A
66. Orthogonal Procrustes Problem Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] Post 5 [ 0.239 -0.646 0.002 -0.702 … ] Post 6 [ -0.612 -0.408 0.052 0.064 … ] Post 7 [ 0.139 0.118 -0.142 -0.157 … ] … [ … ] Post Embedding Vectors Post 3 [ -0.242 0.218 0.848 -0.887 … ] Post 4 [ 0.581 -0.859 0.006 -0.598 … ] Post 5 [ 0.344 -0.834 -0.651 0.524 … ] Post 6 [ 0.255 0.963 -0.127 -0.959 … ] Post 7 [ -0.299 0.808 0.677 -0.604 … ] Post 8 [ 0.992 -0.795 0.062 -0.490 … ] Post 9 [ 0.855 0.622 -0.793 -0.329 … ] … [ … ] t=0 t=1 A B 1-to-1 correspondence of points find orthogonal matrix W that maps B into A
67. Orthogonal Procrustes Problem Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] Post 5 [ 0.239 -0.646 0.002 -0.702 … ] Post 6 [ -0.612 -0.408 0.052 0.064 … ] Post 7 [ 0.139 0.118 -0.142 -0.157 … ] … [ … ] Post Embedding Vectors Post 3 [ -0.242 0.218 0.848 -0.887 … ] Post 4 [ 0.581 -0.859 0.006 -0.598 … ] Post 5 [ 0.344 -0.834 -0.651 0.524 … ] Post 6 [ 0.255 0.963 -0.127 -0.959 … ] Post 7 [ -0.299 0.808 0.677 -0.604 … ] Post 8 [ 0.992 -0.795 0.062 -0.490 … ] Post 9 [ 0.855 0.622 -0.793 -0.329 … ] … [ … ] t=0 t=1 1-to-1 correspondence of points find orthogonal matrix W that maps B into A transform whole t=1 embedding matrix using W
68. Orthogonal Procrustes Problem Post Embedding Vectors Post 1 [ -0.242 0.218 0.848 -0.887 … ] Post 2 [ 0.581 -0.859 0.006 -0.598 … ] Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] Post 5 [ 0.239 -0.646 0.002 -0.702 … ] Post 6 [ -0.612 -0.408 0.052 0.064 … ] Post 7 [ 0.139 0.118 -0.142 -0.157 … ] … [ … ] Post Embedding Vectors Post 3 [ 0.344 -0.834 -0.651 0.524 … ] Post 4 [ 0.255 0.963 -0.127 -0.959 … ] Post 5 [ 0.239 -0.646 0.002 -0.702 … ] Post 6 [ -0.612 -0.408 0.052 0.064 … ] Post 7 [ 0.139 0.118 -0.142 -0.157 … ] Post 8 [ 0.992 -0.795 0.062 -0.490 … ] Post 9 [ 0.855 0.622 -0.793 -0.329 … ] … [ … ] t=0 t=1′  t=0 and t=1′  are in same vector space add embeddings only in t=1′  to t=0 Aligned Embeddings!
69. Summary > Understand the nature of your data > Dual importance of quantitative and qualitative evaluation > “Perfection is not attainable. But if we chase perfection, we can catch excellence.” - Vince Lombardi > Model architecture is essential > Understanding your evaluation metric
70. Summary > Understand the nature of your data > Dual importance of quantitative and qualitative evaluation > “Perfection is not attainable. But if we chase perfection, we can catch excellence.” - Vince Lombardi > Model architecture is essential > Understanding your evaluation metric
71. Summary > Understand the nature of your data > Dual importance of quantitative and qualitative evaluation > “Perfection is not attainable. But if we chase perfection, we can catch excellence.” - Vince Lombardi > Model architecture is essential > Understanding your evaluation metric
72. Summary > Understand the nature of your data > Dual importance of quantitative and qualitative evaluation > “Perfection is not attainable. But if we chase perfection, we can catch excellence.” - Vince Lombardi > Model architecture is essential > Understanding your evaluation metric
73. Thank You