The course on recommender systems conducted in National Research University - Higher School of Economics (Moscow, Russia). Academic year 2024/2025.

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

The final grade is calculated as follows:

0.3 * Home Assignment + 0.3 * Quizzes + 0.4 * Exam

where Home Assignments - 5 home assignments in Jupyter Notebook (max 10 points). Quizzes - 19 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 (Lecture 1, Seminar 1)
2. Similarity (neighborhood) based and linear approaches (Lecture 2, Seminar 2)
3. Matrix factorization (Lecture 3, Seminar 3)
4. Collaborative filtering (Lecture 4, Seminar 4)
5. Content and context-based models (Lecture 5, Seminar 5)
6. Hybrid approaches (Lecture 6, Seminar 6)
7. Sequential models for next-item recommendations (Lecture 7, Seminar 7)
8. Context-based recommendations [Canceled]
9. Models for the next-basket recommendations task (Lecture 9, Seminar 9)
10. Autoencoders and variational autoencoders for recommendations (Lecture 10, Seminar 10)
11. Graph and knowledge-graph based models (Lecture 11, Seminar 11)
12. Interpretability and explainability (Lecture 12, Seminar 12)
13. Uplift recommendations (Lecture 13, Seminar 13)
14. Multi-task & cross-domain recommendations (Lecture 14, Seminar 14)
15. RL in RecSys (Lecture 15, Seminar 15)
16. Domain recommendations (multiomodal data) (Lecture 16, Seminar 16)
17. A/B testing and multi-armed bandites. Model monitoring (Lecture 17, Seminar 17)
18. Large scale RecSys (Lecture 18, Seminar 18)
19. Vanilla API service for recommender system (Lecture 19, Seminar 19)
20. Additional applied aspects and trends in recommender systems (Lecture 20, Seminar 20)

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

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