2026/2027

Рекомендательные системы

Статус: Маго-лего

Кто читает: Департамент анализа данных и искусственного интеллекта

Где читается: Факультет компьютерных наук

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Джин Сеунгмин

Язык: русский

Кредиты: 6

Контактные часы: 40

Программа дисциплины

Аннотация

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.

Цель освоения дисциплины

• Introduction to classical and modern models and methods of recommender systems . Expanding the practical skills of a data science specialist .

Планируемые результаты обучения

• Creation of a prototype of a recommender system based on collaborative filtering methods
• Finding patterns in data ( association rules , frequent sets of items and subsequences of events ).
• Building a taste profile of the user and products
• Understanding relevant quality measures in the field of recommender systems
• Conducting practical research in the field of recommender systems

Содержание учебной дисциплины

• Introduction to recommender systems . Taxonomy of recommender systems . Case-study examples . Methods for assessing the quality of recommender systems .
• Collaborative filtering methods . Case-study: User-based and item-based approaches . Bimodal cross-validation . Movie Lens Dataset.
• Frequent sets of goods . Association rules . Case-study: Contextual Advertising.
• Matrix factorization methods . Case-study: Boolean matrix factorization (BMF), non-negative matrix factorization (NMF), singular value decomposition (SVD).
• Frequent ( sub ) event sequences . Transactional data . Problem : Next-basket prediction.
• Hybrid Recommender Systems . Case-Study: Recommending Music Tracks and Radio Stations .
• Advanced methods of matrix and tensor factorization . ALS, SVD++, Factorization Machines.
• Filtering . Case study: Search for visually similar products . Transformers , LLM for recommender systems .
• Spectral clustering and biclustering for collaborative filtering . Case study: Contextual Advertising.
• Developing our own measure of recommendation quality for Top-n lists .

Элементы контроля

• Homework 1. Case-study: user-based and item-based approaches
• Homework 2. Case study: frequent feature sets and association rules .
• Homework 3. Case study: spectral clustering and contextual advertising .
• Homework 4.
• Project . Individual or in small groups

Промежуточная аттестация

• 2026/2027 2nd module
  Final = 0. 6 * DZ + 0. 3 * Project + 0.2 * Exam / Project defense

Список литературы

Рекомендуемая основная литература

• Manouselis, N., Drachsler, H., Verbert, K., Duval, E. Recommender Systems for Learning. – Springer, 2013. – ЭБС Books 24x7.

Рекомендуемая дополнительная литература

• 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

Авторы

• Антропова Лариса Ивановна