This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
Ce cours fait partie de la Spécialisation Systèmes de recommandation
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Spécialisation Systèmes de recommandationUniversité du MinnesotaÀ propos de ce cours
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Approx. 23Â heures pour terminer
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Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol
Compétences que vous acquerrez
- Summary Statistics
- Term Frequency Inverse Document Frequency (TF-IDF)
- Microsoft Excel
- Recommender Systems
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 5 dans le
Niveau intermédiaire
Approx. 23Â heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol
Offert par
Université du Minnesota
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
Programme de cours : ce que vous apprendrez dans ce cours
1Â heure pour terminer
Preface
This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization.
1Â heure pour terminer
2 vidéos (Total 41 min), 1 lecture
4Â heures pour terminer
Introducing Recommender Systems
This module introduces recommender systems in more depth. It includes a detailed taxonomy of the types of recommender systems, and also includes tours of two systems heavily dependent on recommender technology: MovieLens and Amazon.com. There is an introductory assessment in the final lesson to ensure that you understand the core concepts behind recommendations before we start learning how to compute them.
4Â heures pour terminer
9 vidéos (Total 147 min), 2 lectures, 2 quiz
10Â heures pour terminer
Non-Personalized and Stereotype-Based Recommenders
In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension.
10Â heures pour terminer
7 vidéos (Total 111 min), 5 lectures, 9 quiz
3Â heures pour terminer
Content-Based Filtering -- Part I
The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems.
3Â heures pour terminer
8 vidéos (Total 156 min)
6Â heures pour terminer
Content-Based Filtering -- Part II
The assessments for content-based filtering include an assignment where you compute three types of profile and prediction using a spreadsheet and a quiz on the topics covered. The assignment is in three parts -- a written assignment, a video intro, and a "quiz" where you provide answers from your work to be automatically graded.
6Â heures pour terminer
2 vidéos (Total 26 min), 3 lectures, 3 quiz
1Â heure pour terminer
Course Wrap-up
We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization).
1Â heure pour terminer
2 vidéos (Total 45 min), 1 lecture
Avis
- 5 stars60,65Â %
- 4 stars29,43Â %
- 3 stars6,34Â %
- 2 stars1,78Â %
- 1 star1,78Â %
Meilleurs avis pour INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED
par PD24 juin 2017
Great, thorough introduction with tracks for both Java programmers and non-programmers.
par NA6 avr. 2020
The course and its content was quite interesting and easy, so I will be taking the next course in this specialization of Recommender System Specialization
par CC5 juil. 2021
Excellent content, great structured frameworks to understand when / why to use different recommenders
par BS12 févr. 2019
One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.
À propos du Spécialisation Systèmes de recommandation
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
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D'autres questions ? Visitez le Centre d'Aide pour les Étudiants.
