Master Recommender Systems
Learn to design, build, and evaluate recommender systems for commerce and content.
About This Specialization
Follow the suggested order or choose your own.
Designed to help you practice and apply the skills you learn.
Highlight your new skills on your resume or LinkedIn.
- Intermediate Specialization.
- Some related experience required.
Introduction to Recommender Systems: Non-Personalized and Content-BasedUpcoming session: May 8 — Jun 12.
- 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
About the CourseThis 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.
Nearest Neighbor Collaborative FilteringUpcoming session: May 1 — Jun 5.
About the CourseIn this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
Recommender Systems: Evaluation and MetricsUpcoming session: May 8 — Jun 12.
About the CourseIn this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. You will learn how different metrics relate to different user goals and business goals. You will also learn how to rigorously conduct offline evaluations (i.e., how to prepare and sample data, and how to aggregate results). And you will learn about online (experimental) evaluation. At the completion of this course you will have the tools you need to compare different recommender system alternatives for a wide variety of uses.
Matrix Factorization and Advanced TechniquesStarts May 29th 2017
About the CourseIn this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
Recommender Systems CapstoneStarts June 19th 2017
About the Capstone ProjectThis capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance. Learners in the honors track will focus on experimental evaluation of the algorithms against medium sized datasets. The standard track will include a mix of provided results and spreadsheet exploration. Both groups will produce a capstone report documenting the analysis, the selected solution, and the justification for that solution.
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Michael D. Ekstrand