1. A research on Machine learning approach for Adaptive User Interface Generator
We are from 99X technology
Samudra Kanankearachchi
Senior Software
Data Science Specialist

2. Why LizardUI? (Research Problem)

3. Software Aging vs Adaptivity
We often experience unmanaged feature growth makes software products bulky , and difficult to change
Accumulation of feature with aging
Makes software less Adaptive

4. Actual Feature usage against What we delivered
Different Usage Profiles
Random usage
Low usage

5. Research : How do we make a product adaptive
How do Increase the ability change the application based on user context ?

6. Lizard UI Concept (Research Outcome)
(Full

7. Track buyers Statistics
Adaptive Modeling
Monolithic
Use of building blocks
Track buyers Statistics
Adaptive variations will remain .
Less adaptive will not move forward
Increased prediction ability about future demands
One Choice
Difficult to change.
More Verities (Choices)
Rapid Changing Ability
Ability to customize features.

8. Decomposing application into building blocks (Example From Tourism Domain)
We barrowed the concept from Lego Building blocks
Application =
Application = ∑ Building Block (Features + API + DA)

9. Statistical Modeling (Mapping features in to a model)
Usage
Function Y =
Y – Dependent Variable
X1, X2 , X3 ,X4, X5 – Independent Random variables
X1, X2 , X3 ,X4, X5 – Uses a formula map feature into a value (Example Click stream count on the feature

10. Track random usage of features (X1, … X5)

11. (Building Ontologies for Similarity matching ) Clustering Data
Features as a graph
Features as a Hierarches

12. Labeling as per usage Patternsf1 f2 f5
When you have feature usage models you can identify popular usage scenarios f4 f3

13. High level Architecture
Un-supervised Learning
Random
Supervised Learning
Prediction

14. + Selling Application Selling Building Blocks + ML Models
Adaptive Business Layer
Impact on pricing model
People start seeing applications instead of features
Machine Learning Model

15. + +
Complex Business
Simple Business
Complex ML
Simple ML

16. Models for flexible Pricing
Feature Usage Oriented
API Usage Oriented
Seasonal Pricing Models
Context Oriented

17. Waste Reduction
Deliver only necessities

18. Technologies used

19. Technologies used AWS Machine learner AWS Machine learner AWS

20. Future road map Mid 2016 2017 Finding investment Strategies
Commercial grade product
2016
Finding investment Strategies
Mid
2017
Integrate to ISV products

21. References
[1] Wikipedia, "Adaptive user interface", [Online]. Available:
[Accessed: 21- Jan- 2016]. [2] J. Mangalindan, "Amazon’s recommendation secret", Fortune, [Online]. Available:
[Accessed: 21- Jan- 2016].
[3] Developer.ebay.com, "Listing Recommendation API: Users Guide", [Online]. Available: recommendation/Concepts/ListingRecommendationAPIGuide.html. [Accessed: 21- Jan- 2016].
[4] Trouvus.com, "How Does the YouTube Recommendation System Work?", [Online]. Available: [Accessed: 21- Jan- 2016].
[5] Saedsayad.com, "Data Mining", [Online]. Available: [Accessed: 21- Jan- 2016].
[6] Google.lk, "Google Analytics - Mobile, Premium and Free Website Analytics – Google", [Online]. Available: [Accessed: 21- Jan- 2016].
[7] Analytics Platform - Piwik, "Free Web Analytics Software", [Online]. Available: [Accessed: 21- Jan ].
[8] Analytics Platform - Piwik, "What is Piwik? - Analytics Platform - Piwik", [Online]. Available: piwik. [Accessed: 21- Jan- 2016].
[9] Webbistdu.de, "Google Analytics vs. Piwik", [Online]. Available: vs-piwik. [Accessed: 21- Jan- 2016].

22. References (cnt…)
[10] V. Chitraa and D. Davamani, "A Survey on Preprocessing Methods for Web Usage Data", Arxiv.org, [Online]. Available: [Accessed: 21- Jan- 2016].
[11] Openclassroom.stanford.edu, "Machine Learning", [Online]. Available: &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 , 2013.
[13] CreateMutex, "Clustering VS Classification", [Online]. Available: Classification. [Accessed: 21- Jan- 2016].
[14] Scikit-learn.org, "scikit-learn: machine learning in Python — scikit-learn documentation", [Online]. Available: [Accessed: 21- Jan- 2016].
[15] Mlpy.sourceforge.net, "Linear Methods for Classification — mlpy v3.1 documentation", [Online]. Available: [Accessed: 21- Jan- 2016].
[16] Wikipedia, "Regression analysis", [Online]. Available: [Accessed: 21- Jan- 2016].
[17] Mlpy.sourceforge.net, "Large Linear Classification from [LIBLINEAR] — mlpy v3.2 documentation", [Online]. Available: [Accessed: 21- Jan- 2016].
[18] Amazon Web Services, Inc., "Amazon Machine Learning - Predictive Analytics with AWS", [Online]. Available: [Accessed: 21- Jan- 2016].

23. Q & A
Thank You