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Бакалавриат 2019/2020

Рекомендательные системы

Статус: Курс по выбору (Бизнес-информатика)
Направление: 38.03.05. Бизнес-информатика
Когда читается: 4-й курс, 1 модуль
Формат изучения: Blended
Язык: английский
Кредиты: 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

Learning Objectives

  • to introduce the concept of recommender systems
  • to review basic approaches to building recommendations
Expected Learning Outcomes

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

Course Contents

  • Introduction to Recommender Systems
  • Non-Personalized and Stereotype-Based Recommenders
  • Content-Based Filtering
  • Collaborative Filtering
  • Uplift modeling
    Uplift modeling is a predictive modeling technique that directly models the incremental impact of a treatment on a customer's behavior.
Assessment Elements

Assessment Elements

  • non-blocking Completion of recommended online course
  • non-blocking Final test
    Final test contains several multiple-choice questions.
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.6 * Completion of recommended online course + 0.4 * Final test
Bibliography

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.