A talk from 2018 RIGA COMM conference where I cover algorithms we developed to create recommendation system for Latvian e-index. The Latvian e-index is an e-environment rating (level/quality of e-services) in public administration institutions and municipalities. Created and calculated by Ministry of Environmental Protection and Regional Development of the Republic of Latvia (VARAM). In talk I give brief overview of recommendation systems and what approaches those can take, how to consider which ones work in your case and why none of typical approaches doesn't work in this case. Then I present the approach we developed.

1. Finding improvement path
(Recommender system)
Muntis Rudzītis
Machine Learning enthusiast
RIGA COMM, 11.10.2018

2. 2
Emergn Machine Learning Lab
Predictive invoice-to-payment for
Utility company
ML technique:
Random Forest, AdaBoost etc.
ML technique:
Computer vision
Employee detection in production
for Latvian Plywood
ML technique:
Market Basket Analysis
(association rules)
Product recommendation engine
for e-markets

3. Question – “What’s next?”
• Next video/movie
• Next song
• Next JIRA / Redmine / Bugzilla task
• Next thing to learn
Finding optimum and making sure it will lead to better result is hard and highly biased.
3

4. Our solution
• Requires:
• Historical data
• Enough data
• The more input factors, the better
• Rating function (current rating) – can be individualized or not.
4

5. Latvian e-environment
The Latvian e-index is an e-environment rating (level/quality of e-services) in public
administration institutions and municipalities.
Created and calculated by Ministry of Environmental Protection and Regional Development of the Republic
of Latvia (VARAM).
5
What's needed?
• Personalized recommendations for each institution
• Ordered plan of action for e-index improvement
• Visual, end-user-readable representation of the solution
VARAM logo

6. Latvian e-environment
The Latvian e-index is an e-environment rating (level/quality of e-services) in public
administration institutions and municipalities.
6
What we did?
• Data exploration
• Comparison by variation
• Heavy normalization and
transformation
• Clustering? No.
• MinMaxing? No.

7. LV e-environment
• Closeness = similarity in functionality, by variation
• Color = growth of e-index value
• Recommendations based on similar, but better
performing neighbors.
• Action items – based on maximum potential growth in
local vicinity.
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8. 8
https://plot.ly/~akramkovska/11.embed

9. 9
https://plot.ly/~akramkovska/30.embed

10. Recommendation system
• Recommends improvement based upon similar data points
• Recommends 3 (or more) next best steps, calculates potential gain
• Recalculates the dimensions, adjusts the parameters
• Creates next recommendations – creating full improvement path
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11. AI/ML Service description Card
AI/ML Service main Use Case: Company: Emergn
• Software development &
consulting company
• 250 employees located in Riga
AI/ML Service Specification
Improvement path
Uses local similarities to find local
maxima for improvement, by looking
at similar (but better) data points
Prediction
Recommends next
things to do.
Input/Output
Data with high enough
number of parameters
&
ranking function.
Provides next best
step for improvement
with improvement gain
calculated.
Accuracy
Customizable for each
use case – does
provide path.
Expert opinion?
Training
Historical ranked data
with enough
parameters.
~100 data points and
~50+ parameters is
good starting point.
Implementation options:
• Could be implemented and
optimized for use case in
~2 weeks
• No need for specific
infrastructure

12. 12
Questions?
Comments?
Ideas?
If time is short, catch me in the break with your question/idea/use case.

13. Thank you
Muntis Rudzītis
muntis.rudzitis@emergn.com
muntis.rudzitis@gmail.com
Paldies!