user interface adaptation based on user feedback and machine learning
Post on 15-Apr-2017
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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 LabUniversité catholique de Louvain
Promoter:Prof. Jean Vanderdonckt
Jean.vanderdonckt@uclouvain.be
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Adaptation & User Centered Design
Not adapted & not User-centered UI
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Adaptation & User Centered Design
adapted & not User-centered UI
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Adaptation & User Centered Design
Adapted & User-centered UI
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Outline
Motivation
Basic conceptsMethods & Application
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Adaptation rules are staticAdaptation rules are implemented according to a predefined static set of standards, guidelines, and recommendations
Hardly re-adaptableBarely impossible to updateHighly expensive (redevelopment, time, human resources)
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Static rules prevent adaptation
• Dissatisfaction• Frustration• Discouragement• Loss of motivation• …
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Solution: Interaction-based adaptation
Objective:enhancing the end-user influence in the UI definition
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User
Interaction
Feedback
Feedback
analysis
Learning
Recommendation
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Principals typologies to express feedback
Implicit Feedback
Explicit Feedback
Without rating aims With rating aims
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Unified theoretical architecture for adaptation based on ML
Context• User• Platform• Environment
Adaptation Rules
Repository
Adaptation Management
Layer
Perception(tracking tools, sensors…
)
RecommendationFeedback
Reinforcemen
t
EvaluationUpdatin
g Adapting
Perc
eptio
n Lay
er
UI
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The Adaptation Rule Manager
Adaptation Rules
Repository
Trainer-Rule Engine
Learner-Rule Engine
Adaptation Rules Manager
Generated Rules
Rule Engine
Rule Management
Tools
Training Rules
Feedbacks User
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The Adaptation Rule Manager
Adaptation Rules
Repository
Trainer-Rule Engine
Learner-Rule Engine
Adaptation Rules Manager
Generated Rules
Rule Engine
Rule Management
Tools
Training Rules
Feedbacks 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).
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The Adaptation Rule Manager
Adaptation Rules
Repository
Trainer-Rule Engine
Learner-Rule Engine
Adaptation Rules Manager
Generated Rules
Rule Engine
Rule Management
Tools
Training Rules
Feedbacks User
analyzing collected user judgments. Which are intended to serve in a promoting/demoting ranking, Then generate new decision rules , (Learns)
Applications
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Tasks
AUI
CUI
Final UI
Applications
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Tasks
AUI
CUI
Final UI
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Conclusion
State of the arts
Conceptualization
Implantation
Test & Evaluation
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Thank you for your attention.
Nesrine Mezhoudi
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