Explore the intricacies of generating "frequently bought together" recommendations for millions of products in e-commerce platforms. Dive into embedding-based recommendation techniques, offline and online metrics, pipeline development, experimental UI design, and embedding serving layers. Learn about the challenges faced by recommendation teams and discover practical tips for overcoming them. Gain insights into context and arithmetic operation problems, dimension reduction, hyperparameter tuning, continuous delivery mindsets, and the pros and cons of various search algorithms. Understand the importance of manual control mechanisms, post-processing needs, and the balance between complex models and effective tricks. This comprehensive talk covers everything from data preparation and evaluation metrics to performance optimization and timescale considerations, providing valuable knowledge for data engineers, machine learning practitioners, and software engineers working on large-scale recommendation systems.
Frequently Bought Together Recommendations Using Embeddings - Challenges and Solutions
Databricks via YouTube
Overview
Syllabus
Introduction
Embeddings
Contentbased Recommendations
Embedding Recommendations
Scale of Recommendations
Data Preparation
Vertebral Parameters
Evaluation Metrics
UserFriendly Interface
Example of Integrating ML4
Arithmetic Operations
Brand Similarity
Programming Language
Post Filtering Layer
Optimal Values
Post Filtering
Experimental UI
Performance Metrics
Application Performance
Examples
Metrics
Recommendations
Timescale
Taught by
Databricks
