Chapter 19 Systems That Adapt to Their Users

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 involvesome form of learning, inference, or decision making (distinguishesuser-adaptivesystemsfromadaptablesystems) User-adaptivesystemsexamples: • adaptiveUIs • recommendationsystems • 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 e-mails • 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. Adaptingtheinterfacetoindividualabilities • 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 = „recommendingpeople ” • findingfriends • expertsearch • recommendingsocialgroups • Internalsocialnetworkingsites • IBM SocialBlue: • network-based and • interest-basedrecommendation

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 purposeof 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 datato 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 knowledgeabout 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 toinvest time in tests of knowledge or skill, unless these can bepresented in an enjoyable form…

24. Nonexplicit Input • (event) log data: (+) silent, no effortfromtheuser (-) difficulttoutilise • Socialnetworks (like login withFacebook account) (+) useful explicit informationalreadygiven (+) social relations alsoavailable • Sensordata

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

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 supportinginformationacquisition [TED Talk] • Solution: recommendationdictatednotbythecurrentusermodel

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 (usermodel) • Functionality: • Supportingsystemuse • Supportinginformationacquisition • Obtaininginformationaboutusers • Usabilitychallanges