Introduction

Estimated course time: 4 hours

Welcome to Recommendation Systems! We've designed this course to expand your knowledge of recommendation systems and explain different models used in recommendation, including matrix factorization and deep neural networks.

Objectives:

• Describe the purpose of recommendation systems.
• Understand the components of a recommendation system including candidate generation, scoring, and re-ranking.
• Use embeddings to represent items and queries.
• Develop a deeper technical understanding of common techniques used in candidate generation.

Prerequisites

This course assumes you have:

• Completed Machine Learning Crash Course either in-person or self-study, or you have equivalent knowledge.
• Familiarity with linear algebra (inner product, matrix-vector product).

Happy Learning!