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A research on Machine learning approach
for Adaptive User Interface Generator
Samudra Kanankearachchi
Senior Software Architect @99XTechnology
Data Science Specialist
Software Aging vs Adaptivity
Accumulation of feature with aging
Makes software less Adaptive
Actual Feature usage against What we
delivered
Different Usage Profiles
Random usage
Low usage
Research : How do we make a product
adaptive
How do Increase the ability change the application based on user context ?
Adaptive Modeling
Monolithic
Use of building blocks
1. One Choice
2. Difficult to change.
1. More Verities (Choices)
2. Rapid Changing Ability
3. Ability to customize features.
Track buyers Statistics
1. Adaptive variations will
remain .
2. Less adaptive will not move
forward
3. Increased prediction ability
about future demands
Decomposing application into building
blocks (Example From Tourism Domain)
Application
=
∑ Building Block (Features + API + DA)
Statistical Modeling (Mapping features in
to a model)
Usage
Function=
Y
1. Y – Dependent Variable
2. X1, X2 , X3 ,X4, X5 – Independent Random variables
3. X1, X2 , X3 ,X4, X5 – Uses a formula map feature into a value
(Example Click stream count on the feature
Track random usage of features (X1, …
X5)
(Building Ontologies for Similarity matching )
Clustering Data
Features as a graph
Features as a Hierarches
Labeling as per usage Patterns
f2
f1
f5
f3
f4
Prediction
Supervised Learning
Un-supervised Learning
High level Architecture
Building blocks
+
Machine Learning Model
Selling Building Blocks + ML
Models
Adaptive Business Layer
Selling Application
+
+
Complex Business
Complex ML
Simple Business
Simple ML
Models for flexible Pricing
Feature Usage
Oriented
API Usage
Oriented
Seasonal
Pricing Models
Context
Oriented
Waste Reduction
Deliver only necessities
Technologies used
AWS
Machine learner
AWS
Machine learner
AWS
Machine learner
Future road map
2016
Commercial
grade
product
Mid
2017
Finding
investment
Strategies
2017
Integrate
to ISV
products
References

[1] Wikipedia, "Adaptive user interface", 2016. [Online]. Available:

https://en.wikipedia.org/wiki/Adaptive_user_interface. [Accessed: 21- Jan- 2016].
[2] J. Mangalindan, "Amazon’s recommendation secret", Fortune, 2012. [Online]. Available:

http://fortune.com/2012/07/30/amazons-recommendation-secret. [Accessed: 21- Jan- 2016].

[3] Developer.ebay.com, "Listing Recommendation API: Users Guide", 2016. [Online]. Available:
http://developer.ebay.com/devzone/listing- recommendation/Concepts/ListingRecommendationAPIGuide.html.
[Accessed: 21- Jan- 2016].

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
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
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
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
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
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References (cnt…)

[10] V. Chitraa and D. Davamani, "A Survey on Preprocessing Methods for Web Usage Data", Arxiv.org, 2010. [Online].
Available: http://arxiv.org/abs/1004.1257. [Accessed: 21- Jan- 2016].

[11] Openclassroom.stanford.edu, "Machine Learning", 2016. [Online]. Available:
http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning
&doc=exercises/ex8/ex8.html. [Accessed: 21- Jan- 2016].

[12] F. Gouzi, A. Abdellaoui, N. Molinari, E. Pinot, B. Ayoub, D. Laoudj-Chenivesse, J. Cristol, J. Mercier, M. Hayot and C.
Prefaut, "Fiber atrophy, oxidative stress, and oxidative fiber reduction are the attributes of different phenotypes in chronic
obstructive pulmonary disease patients", Journal of Applied Physiology, vol. 115, no. 12, pp. 1796-1805, 2013.

[13] CreateMutex, "Clustering VS Classification", 2012. [Online]. Available: http://arisri.tistory.com/entry/Clustering-VSClassification. [Accessed: 21- Jan- 2016].

[14] Scikit-learn.org, "scikit-learn: machine learning in Python — scikit-learn 0.16.1 documentation", 2016. [Online]. Available:
http://scikit-learn.org. [Accessed: 21- Jan- 2016].

[15] Mlpy.sourceforge.net, "Linear Methods for Classification — mlpy v3.1 documentation", 2016. [Online]. Available:
http://mlpy.sourceforge.net/docs/3.1/lin_class.html. [Accessed: 21- Jan- 2016].

[16] Wikipedia, "Regression analysis", 2016. [Online]. Available: https://en.wikipedia.org/wiki/Regression_analysis. [Accessed:
21- Jan- 2016].

[17] Mlpy.sourceforge.net, "Large Linear Classification from [LIBLINEAR] — mlpy v3.2 documentation", 2016. [Online].
Available: http://mlpy.sourceforge.net/docs/3.2/liblinear.html. [Accessed: 21- Jan- 2016].

[18] Amazon Web Services, Inc., "Amazon Machine Learning - Predictive Analytics with AWS", 2016. [Online]. Available:
https://aws.amazon.com/machine-learning. [Accessed: 21- Jan- 2016].
Thank You