About this Course
4.3
161 ratings
42 reviews
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 12 hours to complete

Suggested: 9 hours/week...
Available languages

English

Subtitles: English...
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 12 hours to complete

Suggested: 9 hours/week...
Available languages

English

Subtitles: English...

Syllabus - What you will learn from this course

Week
1
Hours to complete
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....
Reading
1 video (Total 3 min), 1 reading
Video1 video
Reading1 reading
Course Structure Outline10m
Hours to complete
1 hour to complete

User-User Collaborative Filtering Recommenders Part 1

...
Reading
5 videos (Total 85 min)
Video5 videos
Configuring User-User Collaborative Filtering9m
Influence Limiting and Attack Resistance; Interview with Paul Resnick21m
Trust-Based Recommendation; Interview with Jen Golbeck15m
Impact of Bad Ratings; Interview with Dan Cosley13m
Week
2
Hours to complete
5 hours to complete

User-User Collaborative Filtering Recommenders Part 2

...
Reading
2 videos (Total 13 min), 2 readings, 3 quizzes
Video2 videos
Programming Assignment - Programming User-User Collaborative Filtering4m
Reading2 readings
Assignment Instructions: User-User CF10m
Introducing User-User CF Programming Assignment10m
Quiz2 practice exercises
User-User CF Answer Sheet48m
User-User Collaborative Filtering Quiz20m
Week
3
Hours to complete
1 hour to complete

Item-Item Collaborative Filtering Recommenders Part 1

...
Reading
6 videos (Total 70 min)
Video6 videos
Item-Item Algorithm16m
Item-Item on Unary Data6m
Item-Item Hybrids and Extensions4m
Strengths and Weaknesses of Item-Item Collaborative Filtering9m
Interview with Brad Miller16m
Week
4
Hours to complete
4 hours to complete

Item-Item Collaborative Filtering Recommenders Part 2

...
Reading
2 videos (Total 10 min), 2 readings, 5 quizzes
Video2 videos
Programming Assignment - Programming Item-Item Collaborative Filtering4m
Reading2 readings
Item-Based CF Assignment Instructions10m
Introducing Item-Item CF Programming Assignment10m
Quiz4 practice exercises
Item Based Assignment Part l10m
Item Based Assignment Part II10m
Item Based Assignment Part III10m
Item Based Assignment Part IV10m
Hours to complete
2 hours to complete

Advanced Collaborative Filtering Topics

...
Reading
5 videos (Total 73 min), 1 quiz
Video5 videos
Recommending for Groups: Interview with Anthony Jameson14m
Threat Models11m
Explanations16m
Explanations, Part II: Interview with Nava Tintarev17m
Quiz1 practice exercise
Item-Based and Advanced Collaborative Filtering Topics Quiz20m
4.3
42 ReviewsChevron Right

Top Reviews

By NRFeb 4th 2018

Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!

By ARAug 4th 2017

Awesome as always, Joe and Michael rock. The interview with Brad Miller was stellar, felt like listening to the legends of rock-n-roll!

Instructors

Avatar

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Avatar

Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University

About 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....

About the Recommender Systems Specialization

This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project....
Recommender Systems

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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? Visit the Learner Help Center.