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.
Course 1 of 4 in the Recommender Systems Specialization.
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.
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.
Graded: Closing Quiz: Introducing Recommender Systems
Graded: Honors Track Pre-Quiz
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.
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
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.
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.
Graded: Assignment #2 Answer Form
Graded: Content-Based Filtering
Graded: CBF Programming Assignment
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).
ENROLL IN COURSE