Summary: In this talk we present an innovation to current VAA design which is based on the introduction of a social network element. We refer to this new type of online tool as a Social Voting Advice Application (SVAA). SVAAs extend VAAs by providing (a) community-based recommendations, (b) comparison of users’ political opinions and (c) a channel of user communication. In addition, SVAAs, enriched with data mining modules, can operate as citizen sensors recording the sentiment of the electorate on issues and candidates. Drawing on VAA datasets generated by the Preference Matcher research consortium, we evaluate the results of the first VAA –Choose4Greece– which incorporated social voting features and was launched during the landmark Greek national elections of 2012. We demonstrate how a Social VAA can provide community based features and, at the same time, serve as a citizen sensor. Evaluation of the proposed techniques is realized on a series of datasets collected from various VAAs, including Choose4Greece. The collection is made available on-line in order to promote research in the field. Authors: Ioannis Katakis, Nicolas Tsapatsoulis, Fernando Mendez, Vasiliki Triga, Constantinos Djouvas

1. Social Recommendations
in
Voting Advice Applications
Ioannis Katakis
Fernando Mendez
University of
Athens
University of Zurich
Nicolas Tsapatsoulis
Vasiliki Triga
Costas Djiouvas
Cyprus University of
Technology

2. Summary
Provide Community Recommendations
“How do people with similar ideas vote?”
Machine Learning and Collaborative Filtering
VAA Datasets
Embedded in recent VAAs
Users “Like” social recommendation
Researchers “Like” the data insight
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3. Idea
Provide social (community) recommendation (advice)
Original VAA
Which party
share similar
opinions with
me?
Ioannis Katakis, Social Recommendations in VAAs
Social VAA
How do voters
that share similar
opinions with me
chose to vote?
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4. Recommendation Systems
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5. How do they work?
Identify similar items
Identify similar users
Collaborative filtering (item based – user based)
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6. Data Classification – Supervised Learning
Analyze data (examples) > Learn to predict classes
Orange
Learn “Hidden” Function
𝑓 𝑋 → {𝑂𝑟𝑎𝑛𝑔𝑒, 𝐴𝑝𝑝𝑙𝑒}
Apple
feature values
(e.g. color, shape, size, weight, etc.)
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7. Classification Algorithms
Decision
Trees
Bayesian
Ioannis Katakis, Social Recommendations in VAAs
Neural
Networks
Support
Vector
Machines
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8. Data Clustering – Unsupervised Learning
Identify groups of
similar items
Similarity?
 Euclidean Distance
Algorithms?
 k-Means, EM, etc.
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9. Modeling the VAA problem as ML problem
Features : 30 Questions (totally disagree,…, totally agree)
Class Labels : Vote Intention (political parties)
Examples: Users already in the database
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10. Evaluation
On real VAA datasets
Train – Test split (10 fold cross validation)
Train the dataset on x% of the data
Evaluate (test) on the rest (100-x)%
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11. Approaches
Party coding (not social)
 How VAAs currently work.
Voter-Party opinion
similarity
Average voter
 Average the profiles of the
voters of each party
separately
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12. Approaches
Clustering
Collaborative Filtering
?
k-nearest
Neighbors
Ioannis Katakis, Social Recommendations in VAAs
Classifiers
 Neural
Network
 Support Vector
Machine
 Naïve Bayes
 Decision Tree
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13. Results – basic approaches
Social Approaches > Party Coding
Ioannis Katakis, Social Recommendations in VAAs
Data: Greece, 2011
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14. Results – various classifiers
Support Vector Machines – Best Predictive Performance
Collaborative Filtering - Fast + Accurate
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15. Results – various datasets
Party-Coding < SMO in all datasets
 Difference between datasets maybe correlated with number of
parties, training data size, community agreement
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16. … in the VAA
Also in… Cyprus 2013, Germany 2013, …
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17. What users think…
Like button
likes
satisfaction =
likes + dislikes + neutral
Users seem to like more the social recommendations
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18. What else? – Attribute Selection
Information Gain: ΙG D, a = H D − H T a
H : information entropy
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19. What else? – Data Clustering
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20. Conclusions
Applied Machine Learning Algorithms to VAA data
… to provide social-based advice
… gain data insight
Social-based advice is more accurate than profile matching
VAA users seem to like this feature
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21. More…
Katakis, I.; Tsapatsoulis, N.; Mendez, F.; Triga, V.; Djouvas, C.,
"Social Voting Advice Applications - Definitions, Challenges,
Datasets and Evaluation," IEEE Transactions on Cybernetics
Thank you for
your attention!
www.katakis.eu
ioannis.katakis@gmail.com
@iokat
www.preferencematcher.org
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