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.
About this Course
Skills you will gain
University of 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.
- 5 stars60.45%
- 4 stars29.65%
- 3 stars6.32%
- 2 stars1.78%
- 1 star1.78%
TOP REVIEWS FROM INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED
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.
Excelente curso, presenta una vista amplia de técnicas para la implementación de sistemas de recomendación, lo recomiendo totalmente.
Well-designed assignments and instructive programming exercises in the honors track.
Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).
About the Recommender Systems Specialization
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
How does this course relate to the prior versions of "Introduction to Recommender Systems"?
More questions? Visit the Learner Help Center.