HIVK03416U  Filtering Technology and Filter Bubbles, Elective module

Volume 2018/2019
Content

The course is designed to introduce students to the technologies available to filter information and to the context within which these technologies operate. Information filtering is a process that removes redundant or unwanted information from an information stream using (semi)automated or computerized methods prior to presentation to a human user. Its main goal is the management of the information overload through the delivery of information that the user is likely to find interesting or useful.

During this course we will explore the technology, the psychology, and the societal relevance of filtering. We will practice with different technologies, such as content-based and collaborative filtering, and learn how to apply them in search and recommender systems. We will learn to evaluate these technologies from both a systems and users perspective, and explore which psychological needs underlie successful filtering. Furthermore, we will see how our design decisions relate to contemporary discussions about filter bubbles, echo chambers, and fake news.

Learning Outcome

Competence objectives for the module

The objective of the module is to provide the student with

knowledge and understanding of:

  • A specific subject within library and information science.
  • Relevant theories and methods related to the module's theme.

 

skills in:

  • Identifying and outlining academic issues within library and information science and make these the object of independent analysis.
  • Reflecting critically on theoretical and methodological choices in relation to an academic issue.
  • Expanding on and putting a chosen subject field within library and information science into perspective.

 

competences in:

  • Applying relevant theories and methods to a subject within library and information science.
  • Communicating a scientifically studied issue

 

Academic objectives

The examinee is able to

  • Delimit and deal with and issue within library and information science.
  • Give an account of central theories of relevance to the chosen subject independently and at a level that reflects in-depth knowledge and understanding of the subject's scientific methods.
  • Consider own theoretical and methodological choices critically.
  • Communicate a scientifically studied issue.

Examples of literature that will be used in the course:

  • Hanani, U., Shapira, B., and Shoval, P. (2001). Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11(3):203–259.
  • Brusilovsky, P. and Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In Brusilovsky, P., Kobsa, A., and Nejdl, W., editors, The Adaptive Web: Methods and Strategies of Web Personalization, volume 4321 of Lecture Notes in Computer Science, pages 3–53. Springer, Berlin / Heidelberg.
  • Knijnenburg, B., Willemsen, M., Gantner, Z., Soncu, H., and Newell C. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4):441–504, 2012.
  • DiFranzo, D. and Gloria-Garcia, K. (2017). Filter bubbles and fake news. XRDS 23, 3 (April 2017), 32-35. Available online.

 

These examples are preliminary, the final collection of literature is yet to be decided upon. But, they should give an idea about the course topics!

Class lectures, guest lectures, class discussions, group work, student presentations and individual work.
Students will work in groups to develop and evaluate a filtering/recommender system. Each student will write a paper describing their approach, results, and contributions.
Students are expected to have a basic understanding of and interest in programming and statistics. We will use Python for exercises.
Written
Individual
Collective
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
Credit
15 ECTS
Type of assessment
Written assignment
Exam language. English
Extent: 15-20 standard pages. For group exam, the txtent is increased by 10 standard pages per extra student.
The written take-home assignment, optional subject, can at the creation of
the course, by the teacher, be arranged as 2-4 individual portfolio exercises
written during the course and subsequently revised prior to the final submission
deadline.
Marking scale
7-point grading scale
Censorship form
No external censorship
Exam period

Winter Exam 2018

Re-exam

February 2019:

Make-up examen: Written take-home assignment, set subject. Can only be completed individually.

Extent: 10-15 standard pages, with 7 days to complete the assignment.

 

  • Category
  • Hours
  • Class Instruction
  • 45
  • Exam
  • 120
  • Preparation
  • 245,8
  • Total
  • 410,8