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.
This course is part of the Recommender Systems Specialization
About this Course
Skills you will gain
- Summary Statistics
- Term Frequency Inverse Document Frequency (TF-IDF)
- Microsoft Excel
- Recommender Systems
Syllabus - What you will learn from this course
Introducing Recommender Systems
Non-Personalized and Stereotype-Based Recommenders
Content-Based Filtering -- Part I
Content-Based Filtering -- Part II
- 5 stars60.76%
- 4 stars29.50%
- 3 stars6.22%
- 2 stars1.75%
- 1 star1.75%
TOP REVIEWS FROM INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED
Excelente curso, presenta una vista amplia de técnicas para la implementación de sistemas de recomendación, lo recomiendo totalmente.
I think I am on the right track to changing my career from java engineer from data scientist, this course is one of the best start point
Please update the specialization, it's 2022, and the course slides are from 2016.
Well-designed assignments and instructive programming exercises in the honors track.
About the Recommender Systems Specialization
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.