Master 2026/2027

Recommender Systems

Type: Elective course (Business Analytics and Big Data Systems)

Delivered by: Department of Business Informatics

Where: HSE Graduate School of Business

When: 2 year, 1, 2 module

Open to: students of all HSE University campuses

Instructors: Seungmin Jin

Language: English

ECTS credits: 6

Contact hours: 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.