Iterative Methodology for Personalization Models Optimization
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Describing the iterative process of optimizing personalized content recommendation models, starting from user preferences optimization to a spark-based large-scale modular machine learning framework.
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Iterative Methodology for Personalization Models Optimization
- 1. Iterative Methodology for Personalization Models Optimization
- 10. 1. 2. 3.
- 15. - -
- 16. User Profile Lab Model Learning Framework
- 18. 1. 2.
- 19. 1.
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- 21. 1.
- 22. → 1.
- 23. 1. 2. a. b.
- 24. 1 2 4 5 3
- 25. 1 2 3 4 5
- 28. 1 4
- 29. - Listings - Real Listings - Clicks - User Profile - Ad Profile 1
- 30. 1
- 31. 4
- 32. 4 1 2
- 33. 4 1 2
- 34. 4 1 2
- 35. 4 1 2
- 37. =
- 38. <
- 40. User Profile Lab Model Learning Framework
- 42. Why User Profile Is Important ● Personalization ● Lookalikes modeling ● Interest Targeting ● and more….
- 43. User Profile Data Model DocId Timestamp Feature Confidence 12 0 Sport Cars 1 42 -1 Sport Cars 1 43 -21 World Cup 1 55 -21 World Cup 1 Offline Profile
- 44. User Profile Data Model DocId TS Feature Co nf 12 0 Sport Cars 1 42 -1 Sport Cars 1 43 -21 World Cup 1 55 -21 World Cup 1 Category Conf Sports Cats 2 Soccer 2 Serving ProfileOffline Profile
- 45. User Profile - Boost Recent X2 DocId TS Feature Co nf 12 0 Sport Cars 1 42 -1 Sport Cars 1 43 -21 World Cup 1 55 -21 World Cup 1 Category Conf Sports Cats 2 Soccer 1 Serving ProfileOffline Profile
- 46. Motivation - User Profile Tweaks ● Is this hypothesis true? ● What is the decay schema? ● Linear in time? ● Exponential in time? ● Potentially many trial & error cycles
- 47. Profile Lab - Basic Flow ● Static dataset of offline profiles ● Sequence of docids for each user ● Static feature mapping all docids ● Lean algo block needed to be implemented ● Transform offline profile to online ● Apply algo piece to generate online profile ● Generate KPIs
- 48. Profile Lab - Cross User KPI
- 49. Profile Lab - Cross User KPI
- 50. Profile Lab - Cross User KPI
- 51. Profile Lab - Cross User KPI
- 52. Profile Lab - Cross User KPI
- 53. Profile Lab - Cross User KPI ● Run cross user test 20K time ● Average and normalize result frequencies
- 54. Profile Lab - Advanced Flow (WIP) Uuid, click, adid, document ids of interactions 11233455, true, 99837377, [11234342, 13424234, 3254534] 56456546, false, 3434888, [11234342, 23432444, 1213333, 23432423] 34564363, true, 11113333,[35245555, 463321111, 19938222] …….
- 55. Profile Lab - Advanced Flow (WIP) ● Static supervised datasets of offline profiles ● Perso - with click ● LAL - in/out segment ● Model is built ● Model performance KPIs (auc etc)
- 57. Motivation: Named Entities & Wikitags
- 58. Motivation: Named Entities & Wikitags ● Very high cardinality ~300-400K ● Precise user taste ● Big potential in perso ● Big money for user segmentation ● Hard to leverage as is
- 59. Machine Learning High Applied Statistics High Student Loans Medium Teaching Medium Technical Tutorial High Recurrent Neural Nets High Andrew Ng High Motivation: Hard To Leverage
- 60. Gender Royal Age Tech Rich Prince Harry 1 0.9 -0.05 0.3 0.9 Queen Elizabeth -1 0.99 0.9 -0.8 0.9 Apple inc. 0.1 -0.03 0.5 0.9 0.9 Machine Learning Students -0.7 0.02 -0.5 0.8 -0.6 Facebook 0 0.01 0.2 0.9 0.87 Embeddings: Dense Representation
- 61. Embeddings: Dense Representation ● Given a high confidence concept in a doc ● Context is other concepts ● Lots of training data in our DocStore ● Many existing libraries: word2vec, glove, starspace etc ● Good embedding model
- 62. Machine Learning High Applied Statistics High Student Loans Medium Teaching Medium Technical Tutorial High Recurrent Neural Nets High Andrew Ng High Embeddings Based Models
- 63. Embeddings Based Models ● Major change in prod model architecture ● High dev costs ● Potential issue with Elastic Static embedding cluster is a good fallback
- 64. Clustering Phase ● Cluster embedding vectors ● Cluster id = doc feature ● Concept vector => cluster id ● Easy integration with current architecture
- 65. Clustering - Many Hyperparameters ● Train embedding model : |D| docs, |C| coordinates ● Quick sanity over embeddings model ● Select most frequent N concepts ● Apply sk-learn clustering analysis method A ● Benchmark clusters using common metrics ● Qualitative cluster analysis look good? ● No: Try different |D|, |C|, N, A ● Yes: Implement test and run on lab
- 66. Clustering - Many Hyperparameters ● Starspace embeddings / Word2Vec ● Embedding Dim - 50, 100, 300 ● Number of Clusters - 1000, 2000, 5000, 10000 ● Clustering Algorithms - k-means, DB-Scan
- 67. Clustering Phase ● Pakistani Cricketers ● Philipino Celebrities ● Norwegian Politics ● Badminton ● Potato Dishes ● Spanish Football
- 68. Clustering Phase
- 69. Clustering Phase
- 70. Results - WEC vs WikiTags First WEC model
- 71. ● Word2Vec on 3 days data ● Only features which conf > 0.3 ● No long tail clustering (freq>100) Results - WEC vs Categories
- 72. Training with Modeling Framework
- 73. Training with Modeling Framework
- 74. Thank You