About this Course

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Learner Career Outcomes

60%

started a new career after completing these courses

40%

got a tangible career benefit from this course

12%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 20 hours to complete
English
Subtitles: English

Skills you will gain

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

Learner Career Outcomes

60%

started a new career after completing these courses

40%

got a tangible career benefit from this course

12%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 20 hours to complete
English
Subtitles: English

Offered by

University of Minnesota logo

University of Minnesota

Syllabus - What you will learn from this course

Content RatingThumbs Up90%(1,876 ratings)Info
Week
1

Week 1

1 hour to complete

Preface

1 hour to complete
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

3 hours to complete
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

Week 2

7 hours to complete

Non-Personalized and Stereotype-Based Recommenders

7 hours to complete
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

Week 3

3 hours to complete

Content-Based Filtering -- Part I

3 hours to complete
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

Week 4

6 hours to complete

Content-Based Filtering -- Part II

6 hours to complete
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

1 hour to complete
2 videos (Total 45 min), 1 reading
2 videos
Psychology of Preference & Rating -- Interview with Martijn Willemsen31m
1 reading
Related Readings10m

Reviews

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

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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

  • 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.
  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • 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.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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