Master 2024/2025

Recommender Systems

Type: Elective course (Data Science)

Area of studies: Applied Mathematics and Informatics

Delivered by: School of Data Analysis and Artificial Intelligence

Where: Faculty of Computer Science

When: 2 year, 1, 2 module

Mode of studies: offline

Open to: students of all HSE University campuses

Instructors: Dmitry I. Ignatov

Master’s programme: Data Science

Language: English

ECTS credits: 6

Course Syllabus

Abstract

In this course, we will introduce the problem of building a recommender system (RS) and its relation to other domains of machine learning and information retrieval. We will start by providing an overview of classical approaches for constructing RSs, including content-based and collaborative filtering via matrix factorization. Additionally, we will discuss the metrics and validation schemes commonly employed in RS development.Moving forward, we will delve into modern neural architectures specifically designed for recommender systems. Furthermore, we will explore various techniques frequently utilized in the industry, such as session-based recommender systems, two-stage RSs, and online RSs. Lastly, we may touch upon additional topics of common interest.