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Introduction | Machine Learning | Google for Developers

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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.

• 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.

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!

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-02-27 UTC.

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