Course Description
Fundamental concepts and techniques in recommender systems: similarity models, non-personalized, content-based, and hybrid systems; association rules mining; collaborative filtering: user-, item-, and graph-based models; matrix factorization; graph recommenders, sequential recommenders, evaluation of recommender systems.
Prerequisites
Corequisites
Schedule
| This Course was not Offered During Fall 2025 Term |
| This Course was not Offered During Winter 2026 Term |
| The tentative timetable is not yet available for the Spring/Summer 2026 Term |
| The tentative timetable is not yet available for the Fall 2026 Term |
