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.
Unified Mathematical Model
Skills You'll Learn
Recommender Systems, Summary Statistics, Data Analysis, Term Frequency Inverse Document Frequency (TF-IDF), Probability & Statistics, Applied Machine Learning, Machine Learning, Microsoft Excel
Reviews
4.4 (648 ratings)
- 5 stars60.33%
- 4 stars29.32%
- 3 stars6.48%
- 2 stars2%
- 1 star1.85%
DP
Dec 8, 2017
Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).
IP
Sep 19, 2016
it's a fantastic course that gives you a good idea of what the objectives of recommender systems are and some intuition on the way how it can be accomplished.
From the lesson
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).
Taught By
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Michael D. Ekstrand
Assistant Professor