Presentation Outline
User Interface Adaptation Based
onMotivation
User Feedbacks and Machine
Learning
Basic concept
Bakground
Promotor:
Prof. Jean Vanderdonckt
Nesrine MEZHOUDI
Futur work
[email protected]
[email protected]
Conclusion Louvain Interaction Lab
Université catholique de Louvain
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Adaptation
User-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-adaptable
 Barely impossible to update
 Highly 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-cycle
 Starting from the user interface definition
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Well-rounded feedback topology
Fig. 21. Emoticon answers with an argument.
During this two-week experiment the participants got 2–4 automatic (multiple
choice or emoticons) questions per day (in total 28 questions) via mobile phone
with sound alarm. Both experiments illustrate that this Mobile Feedback method
is very fast and easy to use for users but after while it starts to annoy because it
interrupts the user unnecessarily.
In this version we improved the emoticon set (Fig. 22) by taking Sleepy
emoticon off and leaving the middle place empty. When the user got a question
the cursor was in the middle, and this empty place made sure that the user did not
select an emoticon by chance just by pressing the cursor once. Now he had to
move the cursor and then make a selection.
Explicit Feedback
Happy
Humor
Superhappy
Neutral
Amazed
Furious
Distressed
Angry
Fig. 22. Descriptions of emoticons.
6.1.4 Evaluation of the electronic Experience-Diary
Without rating aims
In this case, we also wanted to use a diary in order to get more in-depth
experiences than just using the mobile feedback method. Based on lessons learnt
from case 3, we did not give the users a paper diary, because we wanted to control
With rating aims
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Implicit Feedback
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Unified theoretical architecture for
adaptation based on ML
Evaluation
Reinforcement
• User
• Platform
• Environment
Recommendation
Perception
Context
Feedback
(tracking tools, sensors…)
UI
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Adaptation Rule Manager
Trainer-Rule
Engine
Training
Rules
Feedback
s
User
Learner-Rule
Engine
Adaptation
Rules
Repository
Rule
Management
Tools
Generated
Rules
Rule Engine
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Adaptation Rule Manager
Trainer-Rule
Engine
Adaptation
Rules
Repository
Training
Rules
Feedback
s
User
Learner-Rule
Engine
Rule
(1)
Executing pre-existed adaptation
Generated
Management
Rules serving as a training set to (2)
rules,
Tools
detect a pattern of user behavior
throughout his feedbacks. Besides,
(3) coming up with statistics and
(promote/demote) ranking for the
Rule Engine Learner Rule Engine (RLE).
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Adaptation Rule Manager
Trainer-Rule
Engine
Training
Rules
Feedback
s
User
Learner-Rule
Engine
Rule
Adaptation
Generated
Management
Rules
Rules
Tools
Repository
analyzing collected user judgments.
Which are intended to serve in a
promoting/demoting ranking, Then
generate new decision rules
Rule , Engine
(Learns)
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learning based adaptation to improve the validity o
Abstract User
Interface. Several algorithms were de
Potential
applications
for the AUI definition however a lack of validity
consistency control still arise (figure3).
Tasks
AUI
CUI
Final
UI
Figure 3. A tasks grouping sample
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techniques were already explored [5], ML still promising t
emphases new adapting scenario for widget selection in th
Potential
applications
concrete UI, which is full of adaptation rules and guideline
In figure 4 we show a sample for considering adaptatio
guidelines to select a multiple-choice widget selection.
Tasks
AUI
CUI
Final
UI
Figure 4. A Multiple-choice widgets definition for a known
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domain
Time-line
Test & Evaluation
Conceptualization
State of the arts
Implantation
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Thank you for your attention
Nesrine Mezhoudi
[email protected]
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