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).
- Introduction to recommender systems
- Similarity (neighborhood) based and linear approaches
- Matrix & tensor factorization
- Collaborative filtering
- Context-aware and content models
- Hybrid approaches
- Sequential models for next-item recommendations
- Next-basket recommendations
- LLM in recommender systems
- Autoencoders and variational autoencoders for recommendations
- Multi-task & cross-domain recommendations
- Graph and knowledge-graph based models
- Interpretability and explainability
- RL for recommender systems
- A/B testing and multi-armed bandites. Model monitoring
- Vanilla API service for recommender system
All content created for this course is licensed under the MIT License. The materials are published in the public domain.
