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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!
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Last updated 2025-08-25 UTC.
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