About this course: 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.
Who is this class for: This course is appropriate for learners who have a basic understanding of statistics. It can be useful both for those exploring applied machine learning and data mining, and for those focused on technology-supported marketing and commerce.
Created by: University of Minnesota
Taught by: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Taught by: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| Basic Info | Course 1 of 5 in the Recommender Systems Specialization |
| Level | Intermediate |
| Commitment | 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. |
| Language | English |
| Hardware Req | For honors track must be able to run substantial computations using Java (e.g., 4GB or more ram). |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Preface
This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization.
2 videos, 1 reading
- Video: Intro to Course and Specialization
- Reading: Notes on Course Design and Relationship to Prior Courses
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.
9 videos, 2 readings
- Video: Preferences and Ratings
- Video: Predictions and Recommendations
- Video: Taxonomy of Recommenders I
- Video: Taxonomy of Recommenders II
- Video: Tour of Amazon.com
- Video: Recommender Systems: Past, Present and Future
- Reading: About the Honors Track
- Video: Introducing the Honors Track
- Video: Honors: Setting up the development environment
- Reading: Downloads and Resources
Graded: Closing Quiz: Introducing Recommender Systems
Graded: Honors Track Pre-Quiz
WEEK 2
Non-Personalized and Stereotype-Based Recommenders
In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension.
7 videos, 5 readings
- Video: Summary Statistics I
- Video: Summary Statistics II
- Reading: External Readings on Ranking and Scoring
- Video: Demographics and Related Approaches
- Video: Product Association Recommenders
- Reading: Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders
- Video: Assignment #1 Intro Video
- Reading: Assignment Intro: Programming Non-Personalized Recommenders
- Video: Assignment Intro: Programming Non-Personalized Recommenders
- Reading: LensKit Resources
- Reading: Rating Data Information
Graded: Assignment #1: Response #1: Top Movies by Mean Rating
Graded: Assignment #1: Response #2: Top Movies by Count
Graded: Assignment #1: Response #3: Top Movies by Percent Liking
Graded: Assignment #1: Response #4: Association with Toy Story
Graded: Assignment #1: Response #5: Correlation with Toy Story
Graded: Assignment #1: Response #6: Male-Female Differences in Average Rating
Graded: Assignment #1: Response #7: Male-Female differences in Liking
Graded: Non-Personalized Recommenders
Graded: Programmming Non-Personalized Recommenders
WEEK 3
Content-Based Filtering -- Part I
The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems.
8 videos
- Video: TFIDF and Content Filtering
- Video: Content-Based Filtering: Deeper Dive
- Video: Entree Style Recommenders -- Robin Burke Interview
- Video: Case-Based Reasoning -- Interview with Barry Smyth
- Video: Dialog-Based Recommenders -- Interview with Pearl Pu
- Video: Search, Recommendation, and Target Audiences -- Interview with Sole Pera
- Video: Beyond TFIDF -- Interview with Pasquale Lops
WEEK 4
Content-Based Filtering -- Part II
The assessments for content-based filtering include an assignment where you compute three types of profile and prediction using a spreadsheet and a quiz on the topics covered. The assignment is in three parts -- a written assignment, a video intro, and a "quiz" where you provide answers from your work to be automatically graded.
2 videos, 3 readings
- Reading: Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)
- Reading: Tools for Content-Based Filtering
- Reading: CBF Programming Intro
- Video: Honors: Intro to programming assignment
Graded: Assignment #2 Answer Form
Graded: Content-Based Filtering
Graded: CBF Programming Assignment
Course Wrap-up
We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization).
2 videos, 1 reading
- Reading: Related Readings
- Video: Psychology of Preference & Rating -- Interview with Martijn Willemsen
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Creators
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.
Pricing
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
Great introduction to Recommender systems. Really got me thinking about how I could apply them.
Good overview on the recommend-er system.
awesome course.
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АЕ
It would be better to make practice on Python.