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Bienvenue dans les systèmes de recommandation. Nous avons conçu ce cours
pour approfondir vos connaissances sur les systèmes de recommandation et expliquer
différents modèles utilisés dans les recommandations, y compris les modèles
la factorisation et les réseaux de neurones profonds.
Sauf indication contraire, le contenu de cette page est régi par une licence Creative Commons Attribution 4.0, et les échantillons de code sont régis par une licence Apache 2.0. Pour en savoir plus, consultez les Règles du site Google Developers. Java est une marque déposée d'Oracle et/ou de ses sociétés affiliées.
Dernière mise à jour le 2024/07/26 (UTC).
[[["Facile à comprendre","easyToUnderstand","thumb-up"],["J'ai pu résoudre mon problème","solvedMyProblem","thumb-up"],["Autre","otherUp","thumb-up"]],[["Il n'y a pas l'information dont j'ai besoin","missingTheInformationINeed","thumb-down"],["Trop compliqué/Trop d'étapes","tooComplicatedTooManySteps","thumb-down"],["Obsolète","outOfDate","thumb-down"],["Problème de traduction","translationIssue","thumb-down"],["Mauvais exemple/Erreur de code","samplesCodeIssue","thumb-down"],["Autre","otherDown","thumb-down"]],["Dernière mise à jour le 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!*"]]
