Bachelor 2019/2020

Recommender Systems

Type: Elective course (Business Informatics)

Area of studies: Business Informatics

Delivered by: Department of Innovation and Business in Information Technologies

Where: Graduate School of Business

When: 4 year, 1 module

Mode of studies: distance learning

Instructors: Vitaly Silchev

Language: English

ECTS credits: 4

Contact hours: 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 modeling
  Uplift 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 test
  Final 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.