University of Minnesota
Introduction to Recommender Systems: Non-Personalized and Content-Based
University of Minnesota

Introduction to Recommender Systems: Non-Personalized and Content-Based

This course is part of Recommender Systems Specialization

Taught in English

Some content may not be translated

Joseph A Konstan
Michael D. Ekstrand

Instructors: Joseph A Konstan

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Course

Gain insight into a topic and learn the fundamentals

4.4

(638 reviews)

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89%

Intermediate level
Some related experience required
23 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

12 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(638 reviews)

|

89%

Intermediate level
Some related experience required
23 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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 quizzes

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 quizzes1 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 quizzes1 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 (60 ratings)
Joseph A Konstan
University of Minnesota
11 Courses205,931 learners
Michael D. Ekstrand
University of Minnesota
6 Courses108,112 learners

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4.4

638 reviews

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Reviewed on Dec 7, 2017

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