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[null,null,["Cập nhật lần gần đây nhất: 2024-07-26 UTC."],[[["\u003cp\u003eThis course provides a comprehensive overview of recommendation systems and their various models, including matrix factorization and deep neural networks.\u003c/p\u003e\n"],["\u003cp\u003eLearners will gain an understanding of the key components of recommendation systems, such as candidate generation, scoring, and re-ranking, as well as the use of embeddings.\u003c/p\u003e\n"],["\u003cp\u003eThe course requires prior knowledge of machine learning concepts and familiarity with linear algebra.\u003c/p\u003e\n"],["\u003cp\u003eUpon completion, learners should be able to describe the purpose of recommendation systems and develop a deeper understanding of common techniques used in candidate generation.\u003c/p\u003e\n"],["\u003cp\u003eThe estimated time commitment for this course is approximately 4 hours.\u003c/p\u003e\n"]]],[],null,["# Introduction\n\n\u003cbr /\u003e\n\n| **Estimated course time:** 4 hours\n\nWelcome to **Recommendation Systems**! We've designed this course\nto expand your knowledge of recommendation systems and explain\ndifferent models used in recommendation, including matrix\nfactorization and deep neural networks.\n| **Objectives:**\n|\n| - Describe the purpose of recommendation systems.\n| - Understand the components of a recommendation system including candidate generation, scoring, and re-ranking.\n| - Use embeddings to represent items and queries.\n| - Develop a deeper technical understanding of common techniques used in candidate generation.\n\nPrerequisites\n-------------\n\nThis course assumes you have:\n\n- Completed [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/) either in-person or self-study, or you have equivalent knowledge.\n- Familiarity with linear algebra (inner product, matrix-vector product).\n\n*Happy Learning!*"]]
