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Research Topics in Recommender Systems

Studienivå (studiesyklus)

Master level

Undervisningssemester

Irregular

Mål og innhald

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.

Læringsutbyte

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

Krav til forkunnskapar

European (three-year) Bachelor's degree in information science or similar degree in ICT, covering basic programming skills.

Krav til studierett

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.

Arbeids- og undervisningsformer

Lectures, and work with assignments, presentations and discussions. Parts of the course may be taught at a distance.

Obligatorisk undervisningsaktivitet

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

Vurderingsformer

  • Individual oral exam (30%)
  • Practical group assignment paper (70%)

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

Karakterskala

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

Vurderingssemester

Assessment in teaching semester

Emneevaluering

INFO345 is evaluated by students every three years and by the department every year.

Kontakt

Kontaktinformasjon

Student advisor: Studierettleiar@ifi.uib.no

Tlf 55 58 41 17

Eksamensinformasjon

  • Vurderingsordning: Gruppeoppgåve og munnleg eksamen

    Trekkfrist
    19.11.2018
    • Eksamensdel: Gruppeoppgåve

      Innleveringsfrist
      03.12.2018, 14:00
      Eksamenssystem
      Inspera
      Digital eksamen
    • Eksamensdel: Munnleg eksamen

      Eksamensperiode
      10.12.2018–11.12.2018