We live in an era of content overload. There is far too much content available online for humans to sort through it individually. This is why most of what we see on the web is picked by recommendation algorithms. These algorithms base their recommendations on (explicit or implicit) feedback from the users. In this project, you will study how to combine different algorithms and how to improve them in order to increase the quality of recommendations.

[1] Mnih, Andriy, and Russ R. Salakhutdinov. "Probabilistic matrix factorization." Advances in neural information processing systems 20 (2007).

[2] Maystre, Lucas. Efficient learning from comparisons. No. THESIS. EPFL, 2018. (Introduction)

[3] Fageot, J., Farhadkhani, S., Hoang, L. N., & Villemaud, O. .Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons. In Proceedings of the AAAI Conference on Artificial Intelligence (2024)

Requirements :

Python , PyTorch

Basis of linear algebra

Probabilities