5.00 credits
30.0 h
Q2
Teacher(s)
Vande Kerckhove Corentin;
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
Lectures
Practical assignments, exercises and projects integrated into the course
Some parts might be lectured in English.
Practical assignments, exercises and projects integrated into the course
Some parts might be lectured in English.
Evaluation methods
Continuous evaluation (no exam session) including projects and exercises verifying that the students master the different competences.
The competences will be detailed during the first lecture.
The competences will be detailed during the first lecture.
Online resources
Online ressources are available at https://moodleucl.uclouvain.be/
Lecture name : MLSMM2156 - Systèmes de recommandation
Key : communicated at the first class
Brief introduction: https://tryolabs.com/blog/introduction-to-recommender-systems/
General overview: https://link.springer.com/book/10.1007%2F978-3-319-29659-3
Lecture name : MLSMM2156 - Systèmes de recommandation
Key : communicated at the first class
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
Aggarwal, Charu C.. “Recommender Systems.” Springer International Publishing (2016).
Faculty or entity
CLSM
