Level of Study
Objectives and Content
This course offers an overview of approaches to develop and evaluate state-of-the-art recommender system methods. In particular, this course makes an extensive introduction to current algorithmic approaches for generating personalized recommender approaches, such as collaborative and content-based filtering, as well as more advanced methods such as hybrid recommender approaches, context-aware methods and approaches relying on machine learning techniques. The course will also discuss in detail how to evaluate recommender systems from an algorithmic and an interface perspective and what needs to be considered when adopting standard recommender approaches to particular domains or use cases.
A student who has completed the course should have the following learning outcomes defined in terms of knowledge, skills and general competence:
- has fundamental knowledge about the central concepts behind recommender systems
- has broad knowledge about state-of-the-art recommender system algorithms
- has extensive knowledge about how to efficiently evaluate recommender systems
- has knowledge about the current research trends in recommender systems
- is able to implement state-of-the-art recommender system algorithms
- can develop their own recommender system
- is able to deploy HCI and machine learning routines to evaluate recommender systems
- is able to teach laymen about how recommender systems work
Required Previous Knowledge
European (three-year) Bachelor's degree in information science or similar degree in ICT, covering basic programming skills.
Access to the Course
Master Programme in Information Science. Students admitted to other Master´s programs and international exchange students may also be qualified to apply for the course.
Teaching and learning methods
Lectures, and work with assignments, presentations and discussions. Parts of the course may be taught at a distance.
Compulsory Assignments and Attendance
- Assignments throughout the semester which must be completed and approved.
- Participation at 80% of course seminars.
Forms of Assessment
- Individual oral exam (30%)
- Practical group assignment paper (70%)
Both the exam and the assignment paper must be done in the teaching semester.
The grading system has a descending scale from A to E for passes and F for fail.
Assessment in teaching semester
INFO345 is evaluated by students every three years and by the department every year.
Student advisor: Studierettleiar@ifi.uib.no
Tlf 55 58 41 17
Type of assessment: Group assignment and individual oral exam
- Withdrawal deadline
Exam part: Group assignment
- Submission deadline
- 03.12.2018, 14:00
- Examination system
- Digital exam
Exam part: Oral exam
- Exam period