Master
2023/2024
Recommender Systems
Type:
Elective course (Business Analytics and Big Data Systems)
Area of studies:
Business Informatics
Delivered by:
Department of Business Informatics
Where:
Graduate School of Business
When:
2 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Dmitry I. Ignatov
Master’s programme:
Business Analytics and Big Data Systems
Language:
English
ECTS credits:
6
Contact hours:
48
Course Syllabus
Abstract
The course is aimed at the formation of sufficient knowledge, skills and competencies for the construction of recommender systems. The course is applied one and covers all advanced topics necessary for successful application both in industry and in academic research. Students of the course will learn the correct formalization of the task, the choice of ranking functions and metrics, the implementation of recommendation ML models in Python - from simple collaborative filtering to modern neural networks.
Learning Objectives
- Formation of knowledge, skills and development skills of recommender systems for research or industrial purposes.
Expected Learning Outcomes
- Explain the key concepts underlying the recommendations
- Demonstrate skills in using meaningful summary statistics
- Сompute product association recommendations
- Build a profile of personal interests
- Build recommendations based on collaborative filtering
- Combine collaborative filtering and content-based recommendations
- Explain the difference between user-based and item-based approaches
- Choose appropriate algorithms for uplift modeling
- 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
Assessment Elements
- HomeworkBuilding a recommender system of a given type based on the provided dataset
- ProjectAs part of the project, students are invited individually or in small groups (no more than three people) to choose a dataset and demonstrate the skills of analyzing a data set and implementing a recommender system based on this data.
- ExamTest with different types of questions
Bibliography
Recommended Core Bibliography
- Parul Aggarwal, Vishal Tomar, & Aditya Kathuria. (2017). Comparing Content Based and Collaborative Filtering in Recommender Systems. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.32D5064E
- 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.