Upon completion of this course, the student is able to:
- analyze and explain the need for adaptation
- choose user, context, group characteristics to model
- decide how to model these, critically analyzing the (dis)advantages of using different techniques
- design adaptation mechanisms, critically analyzing the (dis) advantages of using different techniques
- explain and show the user experience of an adaptive system
- model the implementation of an adaptive system
- consider quality aspects of an adaptive system and decide which to focus on and how to measure those
- evaluate an adaptive system
- reflect on privacy and ethical issues
Assessment
This course is assessed via two course work assignments (in group work) and an final exam (individual work).
The three parts of the assessment contribute to the final grade as follows:
- Assignment 1: written group assignment (35%)
- Assignment 2: group presentation (15%)
- Final exam: individual exam (50%).
More details on the assignments is provided in Blackboard.
A peer effort assessment will be used to allocate individual marks for the two group assignments.
To pass the course each of the three parts of the assessment has to be >=5.5. Note, for the group assignments, the mark after the effort assessment has been taken into account needs to be >=5.5.
A repair test requires at least a 4 for the original test.
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This course is about the design and evaluation of interactive systems that automatically adapt to users and their context.
It discusses the design and evaluation of such systems.
It shows how to build models of users, groups and context, and which characteristics may be useful to model (including for example preferences, ability, personality, affect, inter-personal relationships).
It shows how adaptation algorithms can be inspired by user studies. It covers standard personalization and situationalization concepts as well as standard recommender system techniques such as content-based and collaborative filtering.
It also discusses explanations for adaptive interactive systems and usability issues (such as transparency, scrutability, trust, effectiveness, efficiency, satisfaction, diversity, serendipity, privacy and ethics).
The course content will be presented in the context of various application domains, such as personalized behavior change interventions (e.g., in the health domain) and recommendation in commercial settings (e.g., music, advertising, e-commerce).
Course form
This course has two 2-hour slots a week. These will contain lectures as well as hands-on sessions in which the material of the course is applied to problems.
Literature
The literature consists of scientific articles. A literature list is provided via Blackboard on a weekly basis. In addition, you will need to find articles related to your assignment topic.
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