UMN - Introduction to Recommender Systems: Non-Personalized and Content-Based
- Offered byCoursera
Introduction to Recommender Systems: Non-Personalized and Content-Based at Coursera Overview
Duration | 23 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Recommender Systems: Non-Personalized and Content-Based at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 5 in the Recommender Systems Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 23 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Introduction to Recommender Systems: Non-Personalized and Content-Based at Coursera Course details
- 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.
Introduction to Recommender Systems: Non-Personalized and Content-Based at Coursera Curriculum
Preface
Intro to Recommender Systems
Intro to Course and Specialization
Notes on Course Design and Relationship to Prior Courses
Movielens Tour
Preferences and Ratings
Predictions and Recommendations
Taxonomy of Recommenders I
Taxonomy of Recommenders II
Tour of Amazon.com
Recommender Systems: Past, Present and Future
Introducing the Honors Track
Honors: Setting up the development environment
About the Honors Track
Downloads and Resources
Closing Quiz: Introducing Recommender Systems
Honors Track Pre-Quiz
Non-Personalized and Stereotype-Based Recommenders
Non-Personalized and Stereotype-Based Recommenders
Summary Statistics I
Summary Statistics II
Demographics and Related Approaches
Product Association Recommenders
Assignment #1 Intro Video
Assignment Intro: Programming Non-Personalized Recommenders
External Readings on Ranking and Scoring
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders
Assignment Intro: Programming Non-Personalized Recommenders
LensKit Resources
Rating Data Information
Assignment #1: Response #1: Top Movies by Mean Rating
Assignment #1: Response #2: Top Movies by Count
Assignment #1: Response #3: Top Movies by Percent Liking
Assignment #1: Response #4: Association with Toy Story
Assignment #1: Response #5: Correlation with Toy Story
Assignment #1: Response #6: Male-Female Differences in Average Rating
Assignment #1: Response #7: Male-Female differences in Liking
Non-Personalized Recommenders
Content-Based Filtering -- Part I
Introduction to Content-Based Recommenders
TFIDF and Content Filtering
Content-Based Filtering: Deeper Dive
Entree Style Recommenders -- Robin Burke Interview
Case-Based Reasoning -- Interview with Barry Smyth
Dialog-Based Recommenders -- Interview with Pearl Pu
Search, Recommendation, and Target Audiences -- Interview with Sole Pera
Beyond TFIDF -- Interview with Pasquale Lops
Content-Based Filtering -- Part II
Assignment #2 Introduction: Content-Based Filtering in a Spreadsheet
Honors: Intro to programming assignment
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)
Tools for Content-Based Filtering
CBF Programming Intro
Assignment #2 Answer Form
Content-Based Filtering
Unified Mathematical Model
Psychology of Preference & Rating -- Interview with Martijn Willemsen
Related Readings
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