About

Course objective is to provide comprehensive introduction to the field of Recommender Systems.

• first part of the course will cover theoratical background for RecSys
• next, we will discuss practical aspects of recommender system training and evaluation
• finally we will briefly cover counterfactual evaluation from logged feedback

Syllabus

1. Introduction
2. Neighborhood-Based models
3. Matrix Factorization models
4. Content-based and Hybrid systems
5. Two-level models
6. Neural recommenders
7. Multi-armed bandits
8. Counterfactual evaluation

Labworks

Two homeworks during the course

Grading

Based on homework results

Prerequisites

Machine learning, deep learning