Магистратура
2023/2024
Рекомендательные системы
Статус:
Курс по выбору (Бизнес-аналитика и системы больших данных)
Направление:
38.04.05. Бизнес-информатика
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
2-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Игнатов Дмитрий Игоревич
Прогр. обучения:
Бизнес-аналитика и системы больших данных
Язык:
английский
Кредиты:
6
Контактные часы:
48
Course Syllabus
Abstract
This course equips Master's students in Business Informactics—particularly non-technical majors—with practical skills to design, evaluate, and deploy recommender systems at a professional level. It provides an intuitive, business-oriented understanding of core algorithms (content-based, collaborative filtering, matrix factorization, and graph-based PageRank), while emphasizing low-code approaches using ChatGPT for rapid prototyping and validation.
Key modules include Social Recommender with PageRank for social graph and influence-based recommendations, and Recommender with ChatGPT for conversational interfaces, personalized prompt chains, and explainable AI (XAI) messaging to create "decision-friendly" experiences.
By the end, students will master: aligning recommender strategies with business goals, interpreting data and metrics managerially, low-code LLM prototyping, stakeholder communication and governance, and phased roadmaps for resource-constrained environments. Ultimately, the course reframes recommender systems as operational tools driving business outcomes, integrating analytics, product, and strategy competencies.
Cf. This course evaluates students with the normalized scores.
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.