This course is part of the Recommender Systems Specialization

4.3
stars
298 ratings

Joseph A Konstan +1 more instructor

13,562 already enrolled

Offered By

Recommender Systems SpecializationUniversity of Minnesota

About this Course

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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.

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Course 2 of 5 in the

Recommender Systems Specialization

Approx. 15 hours to complete

English

Subtitles: French, Portuguese (European), Russian, English, Spanish

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 5 in the

Recommender Systems Specialization

Approx. 15 hours to complete

English

Subtitles: French, Portuguese (European), Russian, English, Spanish

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.7/5 (11 Ratings)

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor

Computer Science and Engineering

185,416 Learners

11 Courses

Michael D. Ekstrand

Assistant Professor

Dept. of Computer Science, Boise State University

103,881 Learners

6 Courses

Offered by

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.

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Syllabus - What you will learn from this course

Week

Week 1

13 minutes to complete

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 minutes to complete

1 video (Total 3 min), 1 reading

1 hour to complete

User-User Collaborative Filtering Recommenders Part 1

1 hour to complete

5 videos (Total 85 min)

Week

Week 2

5 hours to complete

User-User Collaborative Filtering Recommenders Part 2

5 hours to complete

2 videos (Total 13 min), 2 readings, 3 quizzes

Week

Week 3

1 hour to complete

Item-Item Collaborative Filtering Recommenders Part 1

1 hour to complete

6 videos (Total 70 min)

Week

Week 4

6 hours to complete

Item-Item Collaborative Filtering Recommenders Part 2

6 hours to complete

2 videos (Total 10 min), 2 readings, 5 quizzes

2 hours to complete

Advanced Collaborative Filtering Topics

2 hours to complete

5 videos (Total 73 min)

Reviews

4.3

68 reviews

5 stars
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4 stars
29.43%
3 stars
11.70%
2 stars
2.34%
1 star
3.01%

TOP REVIEWS FROM NEAREST NEIGHBOR COLLABORATIVE FILTERING

by SKJan 16, 2018

Provides a good overview of item based and user based collaborative filtering approaches.

by JRJul 18, 2021

Very good course, but the quiz on Week 4 is unclear

by ADAug 2, 2017

Loved it...many thanks Prof. Joe for bringing this content to Coursera

by NSDec 11, 2019

i found this course very helpful and informative. it explains the theory while providing real-world examples on recommender systems. the assignment helps in clearing up any confusion with the material

View all reviews

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.

This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.

Frequently Asked Questions

When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Nearest Neighbor Collaborative Filtering

About this Course