The course on recommender systems conducted in National Research University - Higher School of Economics (Moscow, Russia). Academic year 2023/2024.

• Wiki page of this course
• Table with grades
• The code materials for each seminars can be found in the corresponding folders /seminar*.
• To download any folder please use this link.
• Recordings of lectures and seminars (coming soon).
• All questions can be asked in the Telegram chat (the invitation link is available only to students)

The final grade is calculated as follows:

0.3 * Home Assignment + 0.15 * Article Summary + 0.25 * Quizzes + 0.3 * Exam

where Home Assignments - 2 home assignments in Jupyter Notebook (max 10 points). Article Summary - a report on a research paper on Recommender Systems with your critical analysis (max 10 points). Quizzes - 15 weekly quizzes on lecture's and seminars' topics in Google Forms (max 10 points). Exam - oral examination on all topics (max 10 points).

1. Introduction to recommender systems
2. Similarity (neighborhood) based and linear approaches
3. Matrix & tensor factorization
4. Collaborative filtering
5. Context-aware and content models
6. Hybrid approaches
7. Sequential models for next-item recommendations
8. Next-basket recommendations
9. Autoencoders and variational autoencoders for recommendations
10. LLM in recommender systems
11. Multi-task & cross-domain recommendations
12. Graph and knowledge-graph based models
13. Interpretability and explainability
14. RL for recommender systems
15. A/B testing and multi-armed bandites. Model monitoring
16. Vanilla API service for recommender system

• Marina Ananyeva
• Oleg Lashinin
• Denis Krasilnikov
• Alex Milogradskiy

All content created for this course is licensed under the MIT License. The materials are published in the public domain.