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 faz parte do Programa de cursos integrados Sistemas de recomendação
13.191 já se inscreveram
oferecido por
Programa de cursos integrados Sistemas de recomendaçãoUniversidade de MinnesotaUniversidade de MinnesotaInformações sobre o curso
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Certificados compartilháveis
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Curso 2 de 5 no
Aprox. 15 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 5 no
Aprox. 15 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido por
Universidade de MinnesotaUniversidade 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 - O que você aprenderá com este curso
13 minutos para concluir
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 concluir
1 vídeo (Total 3 mín.), 1 leitura
1 hora para concluir
User-User Collaborative Filtering Recommenders Part 1
1 hora para concluir
5 vídeos (Total 85 mín.)
5 horas para concluir
User-User Collaborative Filtering Recommenders Part 2
5 horas para concluir
2 vídeos (Total 13 mín.), 2 leituras, 3 testes
1 hora para concluir
Item-Item Collaborative Filtering Recommenders Part 1
1 hora para concluir
6 vídeos (Total 70 mín.)
6 horas para concluir
Item-Item Collaborative Filtering Recommenders Part 2
6 horas para concluir
2 vídeos (Total 10 mín.), 2 leituras, 5 testes
2 horas para concluir
Advanced Collaborative Filtering Topics
2 horas para concluir
5 vídeos (Total 73 mín.)
Avaliações
- 5 stars53,87%
- 4 stars28,95%
- 3 stars11,78%
- 2 stars2,35%
- 1 star3,03%
Principais avaliações do NEAREST NEIGHBOR COLLABORATIVE FILTERING
por AD2 de ago de 2017
Loved it...many thanks Prof. Joe for bringing this content to Coursera
por EM24 de out de 2016
Very good content ! Very interesting interviews with expert in the field that shows real examples. However the exercise needs a bit more work to be very useful.
por SP13 de fev de 2017
Awesome Professors!Great Material.Very thankful to Coursera for providing this course.
por JR18 de jul de 2021
Very good course, but the quiz on Week 4 is unclear
Sobre Programa de cursos integrados Sistemas de recomendação
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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