Presentation Outline Motivation Basic concept Bakground Futur work Conclusion Nesrine MEZHOUDI [email protected] User Interface Adaptation.

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Presentation Outline

Motivation

Basic concept

Bakground

Futur work

Conclusion

Nesrine MEZHOUDI [email protected]

User Interface Adaptation Based on User Feedbacks and Machine

Learning

Louvain Interaction LabUniversité catholique de Louvain

Promotor:Prof. Jean Vanderdonckt

[email protected]

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AdaptationUser-centered adaptation

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Adaptation User-centeredness

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Adaptation User-centeredness

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Outline

Motivations

Basic concepts

Methods & Application

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Problem: adaptation rules are static

Adaptation rules are implemented according to a predefined and static set of standards, guidelines, and recommendations

Hardly re-adaptableBarely impossible to updateHighly expensive (redevelopment, time, human resources)

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Problem: static rules prevent adaptation

• Dissatisfaction• Frustration• Discouragement• Loss of motivation• …

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Solution: involve the end-user in the user interface development

Throughout the system life-cycle From the early stages of the system life-cycleStarting from the user interface definition

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Well-rounded feedback topology

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

Perce

ptio

n(tra

cking

tools, se

nso

rs…)

Recommendation

FeedbackR

ein

force

men

t

Evalu

atio

n

Updatin

g Adaptin

g

Perc

eptio

n La

yer

UI

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Adaptation Rule Manager

Adaptation Rules

Repository

Trainer-Rule Engine

Learner-Rule Engine

Generated Rules

Rule Engine

Rule Management

Tools

Training Rules

Feedbacks

User

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Adaptation Rule Manager

Adaptation Rules

Repository

Trainer-Rule Engine

Learner-Rule Engine

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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Adaptation Rule Manager

Adaptation Rules

Repository

Trainer-Rule Engine

Learner-Rule Engine

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)

Potential applications

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Tasks

AUI

CUI

Final UI

Potential applications

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Tasks

AUI

CUI

Final UI

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Time-line

State of the arts

Conceptualization

Implantation

Test & Evaluation

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Thank you for your attention

Nesrine Mezhoudi [email protected]