/RecSys_course

Course on recommender systems conducted at the Faculty of Computer Science, National Research University - Higher School of Economics. Academic year 2022-2023.

Primary LanguageJupyter Notebook

RecSys course

The course on recommender systems conducted in National Research University - Higher School of Economics (Moscow, Russia). Academic year 2023 / Курс по рекомендательным системам, который проводится в Национальном исследовательском университете Высшей школе Экономики (Москва). Академический год 2023.

Useful Links

  • Wiki page of this course.
  • The code materials for each seminars can be found in the corresponding folders /seminar*.
  • To download any folder please use this link.
  • Любые вопросы можно задавать в чат с технической поддержкойTG1
  • Table with grades

The most important section

The final grade is calculated as follows:

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

where Home Assignments - 1 home assignments in Jupyter Notebook (max 10 points). Article Summary - конспект/презентация статьи из предложенного списка с критическим анализом (без выступления на семинаре) (max 10 баллов). Weekly Quizzes - 6 квизов по мотивам материалов семинаров, которые сдаются перед началом следующего занятия в Google Forms (ариф.среднее за все квизы, max 10 баллов за каждый). Exam - письменный экзамен в формате решения case-study построения рекомендательной системы для бизнеса (max 10 баллов).

Course Outline / Big plan for small victories

Week 1

Seminar 1-2

  • Examples of RecSys models in production.
  • Formalization of the ranking (recommender systems) task (2 popular types of tasks, 2 types of data sets).
  • Ranking functions (BPR, WARP, RankNET, LambdaRank).
  • Metrics for the quality estimation (Hitrate, Precision@k, Recall@k, MAP@k, NDCG@k).
  • Taxonomy of RecSys approaches ([MF, FM, CF & other general], Content-based [including knowledge graph based, GB for ranking], Context-based, Sequential and session-based models, RL-based models, Hybrid [including two-level cascade]) approaches.
  • Recommended sources on RecSys
  • Hands-on example on the MovieLens dataset: movie recommender system.
  • Basic baselines

Week 2

Seminar 3 - 4

  • Item-based and user-based similarity, similarity metrics.
  • Matrix Factorization (SVD et al.)
  • Collaborative Filtering (ALS and iALS, HALS, NeuralCF)

Week 3

Seminar 5

  • Content-based recommender models
  • DSSM for RecSys
  • Hybrid recommenders taxonomy
  • LightFM (hybrid content model), Lightfm library

Seminar 6

  • Gradient boosting for ranking task
  • Example of cascade recommender model (using gradient boosting on the second level)
  • Important preprocessing steps
  • Cross-validation types

Week 4

Seminar 7

  • Sequential models
  • Next-basket and next-item prediction tasks

Seminar 8

  • Context-aware recommender systems
  • Time-aware и time-dependent models

Week 5

Seminars 9 - 10

  • Autoencoders and Variational autoencoders for RecSys (VAE, Mult-VAE, Multi-VAE, Rec-VAE)

Week 6

Seminar 11

  • Graph-based recommender systems overview
  • Inductive learning (out-of-sample users, cold start problem)
  • GNN, GCN, GraphSage -> PinSage, GAT
  • Explainability & interpretability of recommender systems
  • Knowledge-based graph recommenders

Week 7

Seminar 15 & Seminar 16

  • Vanilla Production-ready RecSys service.

Contributors

License

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