About this Course
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Flexible deadlines

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Intermediate Level

Approx. 16 hours to complete

Suggested: 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. ...

English

Subtitles: English

Skills you will gain

Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 16 hours to complete

Suggested: 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. ...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
1 hour to complete

Preface

2 videos (Total 41 min), 1 reading
2 videos
Intro to Course and Specialization13m
1 reading
Notes on Course Design and Relationship to Prior Courses10m
3 hours to complete

Introducing Recommender Systems

9 videos (Total 147 min), 2 readings, 2 quizzes
9 videos
Preferences and Ratings17m
Predictions and Recommendations16m
Taxonomy of Recommenders I27m
Taxonomy of Recommenders II21m
Tour of Amazon.com21m
Recommender Systems: Past, Present and Future16m
Introducing the Honors Track7m
Honors: Setting up the development environment10m
2 readings
About the Honors Track10m
Downloads and Resources10m
2 practice exercises
Closing Quiz: Introducing Recommender Systems20m
Honors Track Pre-Quiz2m
Week
2
7 hours to complete

Non-Personalized and Stereotype-Based Recommenders

7 videos (Total 111 min), 5 readings, 9 quizzes
7 videos
Summary Statistics I16m
Summary Statistics II22m
Demographics and Related Approaches13m
Product Association Recommenders19m
Assignment #1 Intro Video14m
Assignment Intro: Programming Non-Personalized Recommenders17m
5 readings
External Readings on Ranking and Scoring10m
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10m
Assignment Intro: Programming Non-Personalized Recommenders10m
LensKit Resources10m
Rating Data Information10m
8 practice exercises
Assignment #1: Response #1: Top Movies by Mean Rating10m
Assignment #1: Response #2: Top Movies by Count10m
Assignment #1: Response #3: Top Movies by Percent Liking10m
Assignment #1: Response #4: Association with Toy Story10m
Assignment #1: Response #5: Correlation with Toy Story10m
Assignment #1: Response #6: Male-Female Differences in Average Rating10m
Assignment #1: Response #7: Male-Female differences in Liking8m
Non-Personalized Recommenders20m
Week
3
3 hours to complete

Content-Based Filtering -- Part I

8 videos (Total 156 min)
8 videos
TFIDF and Content Filtering24m
Content-Based Filtering: Deeper Dive26m
Entree Style Recommenders -- Robin Burke Interview13m
Case-Based Reasoning -- Interview with Barry Smyth13m
Dialog-Based Recommenders -- Interview with Pearl Pu21m
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11m
Beyond TFIDF -- Interview with Pasquale Lops21m
Week
4
6 hours to complete

Content-Based Filtering -- Part II

2 videos (Total 26 min), 3 readings, 3 quizzes
2 videos
Honors: Intro to programming assignment10m
3 readings
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1h 20m
Tools for Content-Based Filtering10m
CBF Programming Intro10m
2 practice exercises
Assignment #2 Answer Form20m
Content-Based Filtering20m
1 hour to complete

Course Wrap-up

2 videos (Total 45 min), 1 reading
2 videos
Psychology of Preference & Rating -- Interview with Martijn Willemsen31m
1 reading
Related Readings10m
4.5
87 ReviewsChevron Right

60%

started a new career after completing these courses

40%

got a tangible career benefit from this course

12%

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Top reviews from Introduction to Recommender Systems: Non-Personalized and Content-Based

By BSFeb 13th 2019

One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.

By DPDec 8th 2017

Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).

Instructors

Avatar

Joseph A Konstan

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

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....
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.

  • 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.