Бакалавриат
2019/2020
Рекомендательные системы
Статус:
Курс по выбору (Бизнес-информатика)
Направление:
38.03.05. Бизнес-информатика
Кто читает:
Кафедра инноваций и бизнеса в сфере информационных технологий
Где читается:
Высшая школа бизнеса
Когда читается:
4-й курс, 1 модуль
Формат изучения:
с онлайн-курсом
Преподаватели:
Сильчев Виталий Артемович
Язык:
английский
Кредиты:
4
Контактные часы:
4
Course Syllabus
Abstract
“Introduction to Recommender Systems” is a “blended” course taught in the 4th year of the bachelor’s program “Business Informatics”. The course consists of the on-line part provided by coursera.org (course title – Introduction to Recommender Systems: Non-Personalized and Content-Based, https://www.coursera.org/learn/recommender-systems-introduction) and the off-line part described below. The students are supposed to study the on-line part on their own using the materials available at coursera.org. The off-line part of the course helps students better understand the basics of Recommender Systems by communicating with instructors. The coverage of the off-line part is not limited to the topics of the on-line part and makes special emphasis on the topical issues of the applied fields, which may be hard for self-study.
Learning Objectives
- to introduce the concept of recommender systems
- to review basic approaches to building recommendations
Expected Learning Outcomes
- 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"
Course Contents
- 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.
Assessment Elements
- Completion of recommended online course
- Final testFinal test contains several multiple-choice questions.
Interim Assessment
- Interim assessment (1 module)0.6 * Completion of recommended online course + 0.4 * Final test
Bibliography
Recommended Core Bibliography
- 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
Recommended Additional Bibliography
- Manouselis, N., Drachsler, H., Verbert, K., Duval, E. Recommender Systems for Learning. – Springer, 2013. – ЭБС Books 24x7.