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Te damos la bienvenida a Sistemas de recomendación. Diseñamos este curso
para ampliar tus conocimientos
sobre sistemas de recomendación y explicar
modelos diferentes usados en la recomendación, incluida la
la factorización y las redes neuronales profundas.
Requisitos previos
En este curso, se supone que ya cuentas con los siguientes conocimientos:
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Falta la información que necesito","missingTheInformationINeed","thumb-down"],["Muy complicado o demasiados pasos","tooComplicatedTooManySteps","thumb-down"],["Desactualizado","outOfDate","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Problema con las muestras o los códigos","samplesCodeIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 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!*"]]
