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Product Classification
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Classroom Contents
Machine Learning at Amazon by Rajeev Rastogi
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- 1 Start
- 2 Machine Learning @ Amazon
- 3 Numerous ML Applications
- 4 Address Quality
- 5 Product Packaging
- 6 Product Substitutes
- 7 Product Recommendations
- 8 Product Demand Forecasting
- 9 Product Classification
- 10 Product Matching
- 11 Insights Extraction from Reviews
- 12 Outline
- 13 Amazon Product Pages
- 14 Question & Answering Bot
- 15 Product Feature Questions
- 16 Product Comparison/Compatibility Questions
- 17 Key Challenges
- 18 Learning Semantically Rich Representations
- 19 Results for Different Loss Functions
- 20 Qualitative Results
- 21 Learning Representations with Attention
- 22 Amazon's Product Catalog
- 23 Title Defects
- 24 Image Defects
- 25 Product Attribute Mismatches
- 26 Text Attribute Extraction
- 27 Image Classification/Attribute Extraction
- 28 Mismatch Detection
- 29 Size Recommendation Problem
- 30 Motivation
- 31 Our Approach
- 32 Our Approach Contd
- 33 Bayesian Modeling Benefits
- 34 Intuition
- 35 Data Likelihood
- 36 Generative Model
- 37 Bayesian Inference
- 38 Polya-Gamma Augmentation [Polson et al. 2013]
- 39 Polya-Gamma Augmentation Contd
- 40 Gibbs Sampling Algorithm
- 41 Predictive Distribution
- 42 Experimental Results
- 43 Leveraging Customer and Product Features
- 44 Incorporating Customer Persona
- 45 Summary
- 46 Q&A
