Student Pages
Postgraduate course

Research Topics in Recommender Systems

  • ECTS credits15
  • Teaching semesterSpring, Autumn
  • Course codeINFO345
  • Number of semesters1
  • LanguageEnglish
  • Resources

Level of Study

Master level

Teaching semester


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.

Learning Outcomes

A student who has completed the course should have the following learning outcomes defined in terms of knowledge, skills and general competence:


The candidate

  • 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


The candidate

  • 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 MasterĀ“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.

Compulsory Assignments and Attendance

  • Assignments throughout the semester which must be completed and approved.
  • Participation at 80% of course seminars.

Compulsory requirements are only valid the semester they are approved.

Forms of Assessment

  • Individual written school exam (30%)
  • Practical group assignment project (70%)

Both the exam and the assignment paper must be done in the teaching semester.

Grading Scale

The grading system has a descending scale from A to E for passes and F for fail.

Assessment Semester

Assessment in teaching semester. Only students who have a valid document of absence will be entitled to take a new written exam the following semester.

Course Evaluation

All courses are evaluated according to UiB's system for quality assurance of education.


Contact Information

Student advisor: Studierettleiar@ifi.uib.no

Tlf 55 58 41 17

Exam information

  • For written exams, please note that the start time may change from 09:00 to 15:00 or vice versa until 14 days prior to the exam. Autumn 2020 written exams will be arranged either at home or on campus. Please see course information on MittUiB.

  • Type of assessment: Written exam and group assignment

    Withdrawal deadline
    Examination system
    Digital exam
    • Exam part: Written exam

      24.11.2020, 09:00
      3 hours
      Examination system
      Digital exam
    • Exam part: Group assignment

      Submission deadline
      07.12.2020, 14:00
      Examination system
      Digital exam