Teacher(s)

Sommer Felix;

Language

French

Content

Nowadays, recommender systems play an ever more important role to propose products or services to consumers. Recommending movies, music, news, books, restaurants, financial services, search terms, or contacts, etc. has become a key asset for many companies. Recommender systems can be based on numerous approaches in existence today. This course covers some of these systems with a focus on recommender systems data, collaborative filtering, matrix factorization, and the evaluation of recommender systems.

Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Lectures, Lab work integrated into the course

Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Oral examination based on the lectures as well as a development project

Online resources

Brief introduction: https://tryolabs.com/blog/introduction-to-recommender-systems/
General overview: https://link.springer.com/book/10.1007%2F978-3-319-29659-3

Bibliography

Ekstrand, Michael D., John T. Riedl, and Joseph A. Konstan. "Collaborative filtering recommender systems." Foundations and Trends® in Human–Computer Interaction 4, no. 2 (2011): 81-173.
Aggarwal, Charu C.. “Recommender Systems.” Springer International Publishing (2016).

Faculty or entity

CLSM