1. Presentation Outline Motivation Basic concept Bakground Futur work Conclusion Nesrine MEZHOUDI nesrine.mezhoudi@uclouvain.be User Interface Adaptation Based on User Feedbacks and Machine Learning Louvain Interaction Lab Université catholique de Louvain Promotor: Prof. Jean Vanderdonckt Jean.vanderdonckt@uclouvain.be 1

2. 2  Adaptation  User-centered adaptation

3. 3 Adaptation  User-centeredness

4. 4 Adaptation User-centeredness

5. Outline 5 Motivations Basic concepts Methods & Application

6. Problem: adaptation rules are static 6 Adaptation rules are implemented according to a predefined and static set of standards, guidelines, and recommendations  Hardly re-adaptable  Barely impossible to update  Highly expensive (redevelopment, time, human resources)

7. Problem: static rules prevent adaptation 7 Dissatisfaction Frustration Discouragement Loss of motivation …

8. Solution: involve the end-user in the user interface development  Throughout the system life-cycle  From the early stages of the system life-cycle  Starting from the user interface definition 8

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10. Well-rounded feedback topology 10 Implicit Feedback Explicit Feedback Without rating aims With rating aims 10

11. Unified theoretical architecture for adaptation based on ML 11 Context User Platform Environment Perception (tracking tools, sensors…) Recommendation Feedback Reinforcement Evaluation Evaluation Updatin g Adapting UI

12. Adaptation Rule Manager 12 Adaptation Rules Repository Adaptation Rules Repository Trainer-Rule Engine Learner-Rule Engine Generated Rules Rule Engine Rule Management Tools Training Rules Feedback s User

13. Adaptation Rule Manager 13 Adaptation Rules Repository Adaptation Rules Repository Trainer-Rule Engine Learner-Rule Engine Generated Rules Rule Engine Rule Management Tools Training Rules Feedback s User (1) Executing pre-existed adaptation rules, serving as a training set to (2) detect a pattern of user behavior throughout his feedbacks. Besides, (3) coming up with statistics and (promote/demote) ranking for the Learner Rule Engine (RLE).

14. Adaptation Rule Manager 14 Adaptation Rules Repository Adaptation Rules Repository Trainer-Rule Engine Learner-Rule Engine Generated Rules Rule Engine Rule Management Tools Training Rules Feedback s User analyzing collected user judgments. Which are intended to serve in a promoting/demoting ranking, Then generate new decision rules, (Learns)

15. Potential applications 15 TasksAUICUI Final UI

16. Potential applications 16 TasksAUICUI Final UI

17. Time-line 17 State of the arts ConceptualizationImplantationTest & Evaluation

18. Thank you for your attention Nesrine Mezhoudi nesrine.mezhoudi@uclouvain.be 18