Research Topics in Recommender Systems

Postgraduate course

Course description

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:

Knowledge

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

Skills

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

Level of Study

Master level

Semester of Instruction

Irregular (not taught every year)
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.
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.

The exam assignment will be given in the language of instruction in the course.
The exam answer must be submitted in the same language as the exam assignment.

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.

Retake exam

School exam:

A retake exam is arranged for students with valid absence according to UiBs study regulations § 5-5.If there is a retake exam for students valid absence, students with the following results/absences can register for the exam:

  • Interruption during the exam
  • Fail/failed

Students can register themselves in Studentweb after January 15/August 1

Group assignment:

Students with valid absence as defined in the UiB regulations § 5-5 can apply for an extended submission deadline to eksamen.infomedia@uib.no. The application must be submitted before the deadline for submission has expired.

Course Evaluation
All courses are evaluated according to UiB's system for quality assurance of education.
Examination Support Material
Written exam: Dictionary preapproved by the Faculty