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Introduction to Recommender Systems: Non-Personalized and Content-Based
  • Level Professional
  • Duration 23 hours
  • Course by University of Minnesota
  • Offered by

About

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.

Modules

Introduction

    • 2 Videos
    • 1 Readings
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2 Videos

  • Intro to Recommender Systems
  • Intro to Course and Specialization

1 Readings

  • Notes on Course Design and Relationship to Prior Courses

MovieLens Tour

    • 1 Videos
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1 Videos

  • Movielens Tour

Preferences and Ratings

    • 1 Videos
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1 Videos

  • Preferences and Ratings

Predictions and Recommendations

    • 1 Videos
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1 Videos

  • Predictions and Recommendations

Taxonomy of Recommenders I

    • 1 Videos
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1 Videos

  • Taxonomy of Recommenders I

Taxonomy of Recommenders II

    • 1 Videos
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1 Videos

  • Taxonomy of Recommenders II

Tour of Amazon

    • 1 Videos
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1 Videos

  • Tour of Amazon.com

Recommender Systems: Past, Present and Future

    • 1 Videos
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1 Videos

  • Recommender Systems: Past, Present and Future

Assessment: Module Quiz

    • 1 Assignment
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1 Assignment

  • Closing Quiz: Introducing Recommender Systems

Introducing the Honors Track

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Honors Track Pre-Quiz

2 Videos

  • Introducing the Honors Track
  • Honors: Setting up the development environment

2 Readings

  • About the Honors Track
  • Downloads and Resources

Introduction

    • 1 Videos
Show info about module content

1 Videos

  • Non-Personalized and Stereotype-Based Recommenders

Summary Statistics

    • 2 Videos
    • 1 Readings
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2 Videos

  • Summary Statistics I
  • Summary Statistics II

1 Readings

  • External Readings on Ranking and Scoring

Demographics and Related Approaches

    • 1 Videos
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1 Videos

  • Demographics and Related Approaches

Product Association Recommenders

    • 1 Videos
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1 Videos

  • Product Association Recommenders

Module Assessments

    • 1 Videos
    • 1 Readings
    • 7 Assignment
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7 Assignment

  • 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

1 Videos

  • Assignment #1 Intro Video

1 Readings

  • Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders

Non-Personalized and Stereotyped Recommenders Quiz

    • 1 Assignment
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1 Assignment

  • Non-Personalized Recommenders

Programming Non-Personalized Recommenders with LensKit

    • 1 Videos
    • 3 Readings
    • 1 Programming
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1 Programming

  • Programmming Non-Personalized Recommenders

1 Videos

  • Assignment Intro: Programming Non-Personalized Recommenders

3 Readings

  • Assignment Intro: Programming Non-Personalized Recommenders
  • LensKit Resources
  • Rating Data Information

Content-Based Filtering Using TFIDF

    • 3 Videos
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3 Videos

  • Introduction to Content-Based Recommenders
  • TFIDF and Content Filtering
  • Content-Based Filtering: Deeper Dive

Advanced Content-Based Techniques and Interfaces

    • 5 Videos
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5 Videos

  • 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 Recommender Assignment

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Assignment #2 Answer Form

1 Videos

  • Assignment #2 Introduction: Content-Based Filtering in a Spreadsheet

1 Readings

  • Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)

Quiz on Content-Based Filtering

    • 1 Assignment
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1 Assignment

  • Content-Based Filtering

Tools for CBF

    • 1 Readings
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1 Readings

  • Tools for Content-Based Filtering

CBF Programming Assignment

    • 1 Videos
    • 1 Readings
    • 1 Programming
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1 Programming

  • CBF Programming Assignment

1 Videos

  • Honors: Intro to programming assignment

1 Readings

  • CBF Programming Intro

Broad Topics

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Unified Mathematical Model
  • Psychology of Preference & Rating -- Interview with Martijn Willemsen

1 Readings

  • Related Readings

Auto Summary

Embark on your journey into the world of recommender systems with this professional course from Coursera. Focusing on non-personalized and content-based techniques, it covers essential concepts, methods, and practical tools like LensKit. The course spans 1380 minutes and offers both Starter and Professional subscription options. Ideal for data science and AI enthusiasts, it features expert interviews and interactive exercises to enhance your learning experience.

Joseph A Konstan
Instructors

Joseph A Konstan

Michael D. Ekstrand
Instructors

Michael D. Ekstrand