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Start
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Machine Learning @ Amazon
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Numerous ML Applications
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Address Quality
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Product Packaging
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Product Substitutes
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Product Recommendations
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Product Demand Forecasting
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Product Classification
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Product Matching
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Insights Extraction from Reviews
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Outline
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Amazon Product Pages
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Question & Answering Bot
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Product Feature Questions
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Product Comparison/Compatibility Questions
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Key Challenges
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Learning Semantically Rich Representations
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Results for Different Loss Functions
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Qualitative Results
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Learning Representations with Attention
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Amazon's Product Catalog
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Title Defects
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Image Defects
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Product Attribute Mismatches
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Text Attribute Extraction
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Image Classification/Attribute Extraction
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Mismatch Detection
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Size Recommendation Problem
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Motivation
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Our Approach
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Our Approach Contd
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Bayesian Modeling Benefits
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Intuition
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Data Likelihood
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Generative Model
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Bayesian Inference
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Polya-Gamma Augmentation [Polson et al. 2013]
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Polya-Gamma Augmentation Contd
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Gibbs Sampling Algorithm
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Predictive Distribution
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Experimental Results
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Leveraging Customer and Product Features
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Incorporating Customer Persona
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Summary
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Q&A
Description:
Explore a comprehensive conference talk on machine learning applications at Amazon, delivered by Rajeev Rastogi. Dive into various ML use cases including address quality improvement, product packaging optimization, substitute recommendations, demand forecasting, and product classification. Learn about key challenges in developing question-answering bots for product pages and techniques for learning semantically rich representations. Examine approaches to detect and correct product catalog defects, and understand the size recommendation problem using Bayesian modeling. Gain insights into leveraging customer and product features, incorporating customer personas, and the benefits of Bayesian inference in e-commerce applications.

Machine Learning at Amazon by Rajeev Rastogi

International Centre for Theoretical Sciences
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