1. Chapter 19 Systems That Adapt to Their Users

2. User-adaptive systems
interactive system that adapts its behavior to individual users on the basis of processes of user model acquisition

3. User-adaptive systems
User model acquisition involve some form of learning, inference, or decision making (distinguishes user-adaptive systems from adaptable systems)
User-adaptive systems examples:
adaptive UIs
recommendation systems
personalization

4. SUPPORTING SYSTEM USE

5. Adaptively offering help
In cases where it is not sufficiently obvious to users how they should operate a given application, a help system can adaptively offer information and advice about how to use it
When the user need advice?
What commands the user is familiar with?

6. Adaptively offering help
1980’ Unix commands
1997 Office assistant
2010 CommunityCommands

7. Taking Over Parts of Routine Tasks
Take over routine tasks that are simple but may place heavy demands on a user’s time System learns the patterns in frequent tasks by silently observe the user.

8. Taking Over Parts of Routine Tasks
TaskTracer: the user-adaptive system learns which resources are associated to certain project and recognises which project the user works on currently
Meeting appointment scheduling
Categorisation of s
Smart reply

9. Taking Over Parts of Routine Tasks
precision vs effort to save
Careful user control in the beginning then (after learning) decreasing control. User learns what the system will be able to do successfully

10. Adapting the Interface

11. Adapting the Interface

12. Adapting the interface to individual abilities
People with medical disorder AND
temporary environmental factors:
our fingers are slower in low temperature
ambient noise will affect hearing ability
illuminations impact reading speed
pointing skills are much worse during walking

13. Adapting the interface to individual abilities
Walking UI:
standing and walking Uis
similar but bigger UI items during walking
Difficult to determine when to switch between states
Switching during active usage is anoying

14. SUPPORTING INFORMATION ACQUISITION

15. Helping Users to Find Information
Support for query-based search
Support for browsing
Spontaneous provison of information

16. Recommending Products
recommend products that might be in the interest of the user
based on the user’s history
along with search (not instead)
explanation increases trust

17. Recommending Products
Collaborative filtering and content-based
Critique-based recommender systems:

18. Tailoring Information Presentation
Medical information can be presented in different ways to patients and doctors (interest, ability to understand)
Learning user preferences for information presentation
Color-blind users might require different color palette (adapting for individuals color perception)

19. Bringing people together
= „recommending people ”
finding friends
expert search
recommending social groups
Internal social networking sites
IBM SocialBlue:
network-based and
interest-based recommendation

20. Supporting Learning Adaptive e-learning courses
Within-problem and outer-loop recommendations
Stoichiometry Tutor (2011):
give hints when the user makes a mistake (or asks for help)
behaviour graph for each problem: acceptable paths to a solution along with possible incorrenct steps

21. OBTAINING INFORMATION ABOUT USERS

22. Explicit Self-Reports
user supplies information to the system explicitly for the purpose of allowing the system to adapt
objective properties of the user: age, profession, and place of residence
(+) changing infrequently, (-) typing, (-) privacy concerns.
Restrict requests for personal data to the few pieces of information that the system really requires!
Explain the uses to which the data will be put!

23. Explicit Assessments Self-assessments of interests and knowledge like
the level of the user’s interest in a particular topic,
the level of his or her knowledge about it, or
the importance that the user attaches to a particular evaluation criterion
Responses to test items: tests of particular knowledge or skill. Outside of a learning context, users are likely to hesitate to invest time in tests of knowledge or skill, unless these can be presented in an enjoyable form…

24. Nonexplicit Input (event) log data:
(+) silent, no effort from the user
(-) difficult to utilise
Social networks (like login with Facebook account)
(+) useful explicit information already given
(+) social relations also available
Sensor data

25. Sensor inputs Sensor1 Sensor2 SensorN Environment
SIGNAL PROCESSING
Environment
Current state of the user
User model
Application1
Application2
ApplicationN

26. Usability challenges Predictable and comprehensive Controllable
Non-distractive
Wide range of experience
Privacy preserving

28. Predictability and Comprehensability
The user is able to predict her actions’ influence on the system
Users want to understand the general level of success of the system’s adaptation and they might want to understand why the system was (not) satisfactory in particular cases.

29. Controllability
The user have to be able to approve or prevent particular automatic actions
Especially actions having significant consequences
Only recommendations or asking for approval

30. Distraction
Do not reduce the users’ ability to concentrate on her primary task!

31. Filter bubble System supporting information acquisition [TED Talk]
Solution: recommendation dictated not by the current user model

32. Privacy Security, privacy, trust
Privacy: the ability of individuals to control the terms of under which their personal information is acquired and used (Culnan, 2000)
social-based personalization,
behavioral profiling,
location-based personalization

33. User-adaptive systems
Definition (user model)
Functionality:
Supporting system use
Supporting information acquisition
Obtaining information about users
Usability challanges