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.
Este curso forma parte de Programa especializado: Sistemas de recomendación
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Programa especializado: Sistemas de recomendaciónUniversidad de MinnesotaAcerca de este Curso
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Aprox. 23 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
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Prueba Coursera para negociosHabilidades que obtendrás
- Summary Statistics
- Term Frequency Inverse Document Frequency (TF-IDF)
- Microsoft Excel
- Recommender Systems
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 5 en
Nivel intermedio
Aprox. 23 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?
Prueba Coursera para negocios
ofrecido por
Universidad de 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.
Programa - Qué aprenderás en este curso
1 hora para completar
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.
1 hora para completar
2 videos (Total 41 minutos), 1 lectura
4 horas para completar
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.
4 horas para completar
9 videos (Total 147 minutos), 2 lecturas, 2 cuestionarios
10 horas para completar
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.
10 horas para completar
7 videos (Total 111 minutos), 5 lecturas, 9 cuestionarios
3 horas para completar
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.
3 horas para completar
8 videos (Total 156 minutos)
6 horas para completar
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.
6 horas para completar
2 videos (Total 26 minutos), 3 lecturas, 3 cuestionarios
1 hora para completar
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).
1 hora para completar
2 videos (Total 45 minutos), 1 lectura
Reseñas
- 5 stars60,45 %
- 4 stars29,65 %
- 3 stars6,32 %
- 2 stars1,78 %
- 1 star1,78 %
Principales reseñas sobre INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED
por AR29 de ago. de 2020
Great intro to recommendation systems, the course is well structured and engaging to all students of different backgrounds.
por WH8 de may. de 2018
Great introduction to Recommender systems. Really got me thinking about how I could apply them.
por SG6 de jul. de 2017
Great course. I have already been able to apply what I have learned to me job. Looking forward to the next one.
por BS12 de feb. de 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.
Acerca de Programa especializado: Sistemas de recomendación
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.
Preguntas Frecuentes
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How does this course relate to the prior versions of "Introduction to Recommender Systems"?
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