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.
Introduction to Recommender Systems: Non-Personalized and Content-Based
Introduction to Recommender Systems: Non-Personalized and Content-Based
This course is part of Recommender Systems Specialization
Instructors: Joseph A Konstan
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4.4
(647 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 23 hours
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Assessments
12 assignments
Taught in English
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This course is part of the Recommender Systems Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course
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.
What's included
2 videos1 reading
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.
What's included
9 videos2 readings2 assignments
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.
What's included
7 videos5 readings8 assignments1 programming assignment
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.
What's included
8 videos
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.
What's included
2 videos3 readings2 assignments1 programming assignment
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).
What's included
2 videos1 reading
Instructors
Instructor ratings
4.6 (61 ratings)Offered by
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University of Minnesota
EIT Digital
Sungkyunkwan University
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4.4
647 reviews
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Showing 3 of 647
AS
4
Reviewed on Aug 15, 2019
The course was a good one with content that's understandable. I can't wait to proceed to the next one
EN
5
Reviewed on Jan 5, 2025
The course is very interesting and helpful.
The main issue is that it is outdated. The programming exercises will be much more valuable in Python.
YW
5
Reviewed on Nov 2, 2016
I think this is an amazing course for beginners who are interested in recommender systems, I strongly recommend this course to the students and engineers who are working on recommender systems.
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Frequently asked questions
This specialization is a substantial extension and update of our original introductory course. It involves about 60% new and extended lectures and mostly new assignments and assessments. This course specifically has added material on stereotyped and demographic recommenders and on advanced techniques in content-based recommendation.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
More questions
Financial aid available,