Recommender Systems

This is an introductory course on Recommender Systems. Most of the lectures are accompanied by YouTube videos.

Learning Objectives

1. Familiarize yourselves with the concepts of Recommender Systems.
2. Understand the challenges involved.
3. Be able to recommend appropriate techniques when faced with a recommendation task.

Lectures

• 1. Introduction

• 2. User-User Collaborative Filtering

• 3. Item-Item Collaborative Filtering

• 4. Implicit Feedback and Cold Start

• 5. Matrix Factorization

• 6. Other Model-Based Collaborative Filtering

• 7. Content-Based Recommenders

• 8. System Accuracy

• 9. Evaluation

• 10. Sequence-Aware Recommenders

Acknowledgements

This is a course that I developed while I was at TU Wien. Parts of the lectures use material from various sources, including:

• Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich. Recommender Systems: An Introduction. Cambridge University Press.
• Francesco Ricci, Lior Rokach, Bracha Shapira. Recommender Systems Handbook. 2nd Edition. Springer.
• Amra Delić.