In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
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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Fechas límite flexibles
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Curso 2 de 5 en
Aprox. 15 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
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 2 de 5 en
Aprox. 15 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
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
13 minutos para completar
Preface
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
13 minutos para completar
1 video (Total 3 minutos), 1 lectura
1 hora para completar
User-User Collaborative Filtering Recommenders Part 1
1 hora para completar
5 videos (Total 85 minutos)
5 horas para completar
User-User Collaborative Filtering Recommenders Part 2
5 horas para completar
2 videos (Total 13 minutos), 2 lecturas, 3 cuestionarios
1 hora para completar
Item-Item Collaborative Filtering Recommenders Part 1
1 hora para completar
6 videos (Total 70 minutos)
6 horas para completar
Item-Item Collaborative Filtering Recommenders Part 2
6 horas para completar
2 videos (Total 10 minutos), 2 lecturas, 5 cuestionarios
2 horas para completar
Advanced Collaborative Filtering Topics
2 horas para completar
5 videos (Total 73 minutos)
Reseñas
Principales reseñas sobre NEAREST NEIGHBOR COLLABORATIVE FILTERING
por NS11 de dic. de 2019
i found this course very helpful and informative. it explains the theory while providing real-world examples on recommender systems. the assignment helps in clearing up any confusion with the material
por SS30 de mar. de 2019
Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.
por DG1 de feb. de 2020
I found this course very informative and clears lot of concept in Item based and used based collaborative filtering. Spreadsheet assignment helped me to clearly understand the algorithms.
por NR3 de feb. de 2018
Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!
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.
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