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Recommended Systems - Google Developers
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:
- CompletedMachine Learning Crash Courseeither in-person or self-study, or you have equivalent knowledge.
- Familiarity with linear algebra (inner product, matrix-vector product).
Resource details
| Contributed by: | Generative AI |
| Authored by: |
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| Licence: | More information on licences |
| First contributed: | 02 July 2025 |
| Audience access level: | Full user |
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