This repository contains materials for the Recommender Systems course taught at the Yandex School of Data Analysis. This branch corresponds to the ongoing 2026 spring semester.
- Week 1: Intro
- Lecture: course overview and organizational details, intro to "Recommender Systems" problem
- Seminar: basic recommenders, user-item latent space
- Week 2: Candidate generation & metrics
- Lecture: recsys metrics & candidate generation: classic ML, ANN, mixing
- Seminar: yambda contest overview & baseline solution
- Week 3: Ranking, diversity & metrics
- Lecture: reranking - losses, algorithms, metrics; diversity control and MRR / DPP
- Seminar: classic algorithms (MF, SLIM, EASE); ranking - pool building, undersampling, composite targets
- Week 4: Deep learning for RecSys & neural candidate generation
- Lecture: two-tower architecture, softmax model & sampled softmax loss, contrastive learning, negative sampling and LogQ
- Seminar: paper review on LogQ correction and negative sampling techniques
- Week 5: Neural candidate generation, pt.2
- Lecture: cold start and long-tail, user / item encoding strategies, sequential models, beyond two-tower (GPU retrieval, generative retrieval)
- Seminar: aspects of training neural networks for RecSys
- Week 6: Neural ranking, pt.1
- Lecture: why use DL, feature encoding (categorical, embedding, scalar), model composition
- Seminar: paper review on Piecewise Linear Encoding and Unified Embeddings
- Week 7: Neural ranking, pt.2
- Lecture: feature interaction modelling, MLP, multi-task & knowledge distillation
- Seminar: paper review on DCNv2 architecture
- Week 8: System Design, pt. 1
- Lecture: data architectures, logging, biases, data drift and monitoring
- Seminar: feature storages for different scales
- Week 9: System Design, pt. 2
- Runtime design, candidate funnel, GPU inference, data delivery, controlled degradation, cold start
- Seminar: paper review on GPU retrieval - LiNR, SilverTorch
- Week 10: RecSys Transformers applications
- Lecture: all about sequential models (transformers) on user action history
- Seminar: paper review on PinnerFormer & TransAct (by Pinterest)
- Week 11: Reinforcement Learning in RecSys
- Lecture: all about bandits - algorithms, Thompson sampling, contextual bandits
- Seminar: applications of bandits & off-policy evaluation for e-grocery
- Week 12: Case Studies of Yandex's services
- Lecture: recsys design for e-grocery, movie streaming platform, blogpost & news feed
- Practice: deep dive in Lavka (Yandex's e-grocery) recommender system
- Week 13: Trends in RecSys
- Lecture: state-of-the-art approaches & novel papers in RecSys
- Practice: best contest colutions on yambda retrieval