This course is part of the Recommender Systems Specialization

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Joseph A Konstan +1 more instructor

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Recommender Systems SpecializationUniversity of Minnesota

About this Course

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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. 
After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit.  

In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 1 of 5 in the

Recommender Systems Specialization

Intermediate Level

Approx. 23 hours to complete

English

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Skills you will gain

Summary Statistics
Term Frequency Inverse Document Frequency (TF-IDF)
Microsoft Excel
Recommender Systems

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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 5 in the

Recommender Systems Specialization

Intermediate Level

Approx. 23 hours to complete

English

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Try Coursera for Business

Instructors

Instructor rating

4.62/5 (57 Ratings)

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor

Computer Science and Engineering

191,646 Learners

11 Courses

Michael D. Ekstrand

Assistant Professor

Dept. of Computer Science, Boise State University

105,128 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

Content Rating89%(2,283 ratings)

Week1

Week 1

1 hour to complete

Preface

1 hour to complete

2 videos (Total 41 min), 1 reading

4 hours to complete

Introducing Recommender Systems

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.

4 hours to complete

9 videos (Total 147 min), 2 readings, 2 quizzes

9 videos

Movielens Tour6mPreferences and Ratings17mPredictions and Recommendations16mTaxonomy of Recommenders I27mTaxonomy of Recommenders II21mTour of Amazon.com21mRecommender Systems: Past, Present and Future16mIntroducing the Honors Track7mHonors: Setting up the development environment10m

2 readings

About the Honors Track10mDownloads and Resources10m

2 practice exercises

Closing Quiz: Introducing Recommender Systems20mHonors Track Pre-Quiz30m

Week2

Week 2

10 hours to complete

Non-Personalized and Stereotype-Based Recommenders

10 hours to complete

7 videos (Total 111 min), 5 readings, 9 quizzes

7 videos

Non-Personalized and Stereotype-Based Recommenders7mSummary Statistics I16mSummary Statistics II22mDemographics and Related Approaches13mProduct Association Recommenders19mAssignment #1 Intro Video14mAssignment Intro: Programming Non-Personalized Recommenders17m

5 readings

External Readings on Ranking and Scoring10mAssignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10mAssignment Intro: Programming Non-Personalized Recommenders10mLensKit Resources10mRating Data Information10m

8 practice exercises

Assignment #1: Response #1: Top Movies by Mean Rating30mAssignment #1: Response #2: Top Movies by Count30mAssignment #1: Response #3: Top Movies by Percent Liking30mAssignment #1: Response #4: Association with Toy Story30mAssignment #1: Response #5: Correlation with Toy Story30mAssignment #1: Response #6: Male-Female Differences in Average Rating30mAssignment #1: Response #7: Male-Female differences in Liking30mNon-Personalized Recommenders20m

Week3

Week 3

3 hours to complete

Content-Based Filtering -- Part I

3 hours to complete

8 videos (Total 156 min)

8 videos

Introduction to Content-Based Recommenders23mTFIDF and Content Filtering24mContent-Based Filtering: Deeper Dive26mEntree Style Recommenders -- Robin Burke Interview13mCase-Based Reasoning -- Interview with Barry Smyth13mDialog-Based Recommenders -- Interview with Pearl Pu21mSearch, Recommendation, and Target Audiences -- Interview with Sole Pera11mBeyond TFIDF -- Interview with Pasquale Lops21m

Week4

Week 4

6 hours to complete

Content-Based Filtering -- Part II

6 hours to complete

2 videos (Total 26 min), 3 readings, 3 quizzes

1 hour to complete

Course Wrap-up

1 hour to complete

2 videos (Total 45 min), 1 reading

Reviews

4.5

134 reviews

5 stars
60.76%
4 stars
29.50%
3 stars
6.22%
2 stars
1.75%
1 star
1.75%

TOP REVIEWS FROM INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED

by FCMar 20, 2017

Excelente curso, presenta una vista amplia de técnicas para la implementación de sistemas de recomendación, lo recomiendo totalmente.

by TLJul 21, 2018

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

by LLSep 18, 2022

Please update the specialization, it's 2022, and the course slides are from 2016.

by KROct 8, 2017

Well-designed assignments and instructive programming exercises in the honors track.

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.
How does this course relate to the prior versions of "Introduction to Recommender Systems"?
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.

More questions? Visit the Learner Help Center.

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

About this Course

Skills you will gain