FI:PV254 Recommender Systems - Course Information
PV254 Recommender Systems
Faculty of InformaticsSpring 2025
- Extent and Intensity
- 1/1/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
In-person direct teaching - Teacher(s)
- doc. Mgr. Radek Pelánek, Ph.D. (lecturer)
- Guaranteed by
- doc. Mgr. Radek Pelánek, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Prerequisites
- Programming skills, mathematics at the bachelor level.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 35/30, only registered with preference (fields directly associated with the programme): 15/30 - fields of study / plans the course is directly associated with
- there are 32 fields of study the course is directly associated with, display
- Course objectives
- The goal of the course is to familiarize students with basic techniques and problems in the field of recommender systems. The course is project based - students have practical experience with development of a simple recommender system or with a partial evaluation of a realistic recommender system.
- Learning outcomes
- At the end of the course students will understand the main types of recommender systems and their application domains; be able to apply the basic recommender techniques; be able to implement basic versions of recommender techniques; understand main aspects of evaluation of recommender systems and be able to analyze such evaluations.
- Syllabus
- Recommender systems, motivation, applications in different domains.
- Types of recommender systems: non-personalized, content based, collaborative filtering.
- Techniques and algorithms for recommender systems, particularly with focus on collaborative filtering (user-user, item-item, SVD).
- Evaluation: methodology, types of experiments, evaluation metrics, examples.
- Other aspects of recommender systems (e.g., explanations of recommendations, trust, attacts).
- Case studies (e.g., Amazon, Netflix, Google News, YouTube).
- Educational recommender systems, current research at Faculty of informatics.
- Literature
- Teaching methods
- The course consist of lectures and a project. The project can be either an implementation of a simple recommender system or an evaluation of one of the described techniques on a publicly available dataset.
- Assessment methods
- The main part of the evaluation is a project. The project is typically in groups (2-4 students), but individual projects are also possible.
- Language of instruction
- English
- Further Comments
- The course is taught annually.
The course is taught: every week. - Teacher's information
- http://www.fi.muni.cz/~xpelanek/PV254/
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/spring2025/PV254