- to introduce the concept of recommender systems
- to review basic approaches to building recommendations
- explain the core concepts behind recommendations
- use meaningful summary statistics
- compute product association recommendations
- build a profile of personal interests
- build recommendations based on collaborative filtering
- choose appropriate algorithms for uplift modeling
- explain the difference between user-based and item-based approaches
- combine collaborative filtering and content-based recommendations
- give a definition of the term "uplift"
- Introduction to Recommender Systems
- Non-Personalized and Stereotype-Based Recommenders
- Content-Based Filtering
- Collaborative Filtering
- Uplift modelingUplift modeling is a predictive modeling technique that directly models the incremental impact of a treatment on a customer's behavior.
- Completion of recommended online course
- Final testFinal test contains several multiple-choice questions.
- Interim assessment (1 module)0.6 * Completion of recommended online course + 0.4 * Final test
- Rajaraman, A., & Ullman, J. D. (2012). Mining of Massive Datasets. New York, N.Y.: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=408850
- René Michel, Igor Schnakenburg, & Tobias von Martens. (2019). Targeting Uplift : An Introduction to Net Scores (Vol. 1st ed. 2019). Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2247428
- Manouselis, N., Drachsler, H., Verbert, K., Duval, E. Recommender Systems for Learning. – Springer, 2013. – ЭБС Books 24x7.