Master
2023/2024
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:
Evgeniy Frolov
Master’s programme:
Data Science
Language:
English
ECTS credits:
6
Contact hours:
40
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.