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. Read more