2023/2024

Рекомендательные системы

Статус: Маго-лего

Кто читает: Департамент бизнес-информатики

Когда читается: 1, 2 модуль

Охват аудитории: для своего кампуса

Преподаватели: Игнатов Дмитрий Игоревич

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

• Homework
  Building a recommender system of a given type based on the provided dataset
• Project
  As 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.
• Exam
  Test with different types of questions

Interim Assessment

• 2023/2024 2nd module
  0.2 * Exam + 0.4 * Homework + 0.4 * Project

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.

Authors

• Beklarian Armen Levonovich