Social Recommendations
in
Voting Advice Applications
Ioannis Katakis

Fernando Mendez

University of
Athens

University of...
Summary
Provide Community Recommendations
“How do people with similar ideas vote?”
Machine Learning and Collaborative Filt...
Idea
Provide social (community) recommendation (advice)
Original VAA

Which party
share similar
opinions with
me?

Ioannis...
Recommendation Systems

Ioannis Katakis, Social Recommendations in VAAs

4
How do they work?
Identify similar items

Identify similar users

Collaborative filtering (item based – user based)
Ioanni...
Data Classification – Supervised Learning
Analyze data (examples) > Learn to predict classes
Orange
Learn “Hidden” Functio...
Classification Algorithms
Decision
Trees

Bayesian

Ioannis Katakis, Social Recommendations in VAAs

Neural
Networks

Supp...
Data Clustering – Unsupervised Learning
Identify groups of
similar items

Similarity?
 Euclidean Distance
Algorithms?
...
Modeling the VAA problem as ML problem
Features : 30 Questions (totally disagree,…, totally agree)
Class Labels : Vote Int...
Evaluation
On real VAA datasets

Train – Test split (10 fold cross validation)
Train the dataset on x% of the data
Evalu...
Approaches
Party coding (not social)
 How VAAs currently work.
Voter-Party opinion
similarity
Average voter
 Average the...
Approaches
Clustering

Collaborative Filtering

?
k-nearest
Neighbors

Ioannis Katakis, Social Recommendations in VAAs

Cl...
Results – basic approaches

Social Approaches > Party Coding

Ioannis Katakis, Social Recommendations in VAAs

Data: Greec...
Results – various classifiers

Support Vector Machines – Best Predictive Performance

Collaborative Filtering - Fast + A...
Results – various datasets

Party-Coding < SMO in all datasets
 Difference between datasets maybe correlated with number...
… in the VAA

Also in… Cyprus 2013, Germany 2013, …
Ioannis Katakis, Social Recommendations in VAAs

16
What users think…
Like button

likes
satisfaction =
likes + dislikes + neutral

Users seem to like more the social recomme...
What else? – Attribute Selection

Information Gain: ΙG D, a = H D − H T a
H : information entropy
Ioannis Katakis, Social ...
What else? – Data Clustering

Ioannis Katakis, Social Recommendations in VAAs

19
Conclusions
Applied Machine Learning Algorithms to VAA data
… to provide social-based advice
… gain data insight
Socia...
More…
Katakis, I.; Tsapatsoulis, N.; Mendez, F.; Triga, V.; Djouvas, C.,
"Social Voting Advice Applications - Definitions,...
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Social Recommendations in Voting Advice Applications

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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

Published in: Technology, Education
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  • The idea follows the recent trend of recommendation systems which are actually software applications (usually web applications) tha recommend us items based on previous preference. So good reads is web site that you can enter the books you have read and rate them and it
  • Social Recommendations in Voting Advice Applications

    1. 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. 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 Ioannis Katakis, Social Recommendations in VAAs 2
    3. 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? 3
    4. 4. Recommendation Systems Ioannis Katakis, Social Recommendations in VAAs 4
    5. 5. How do they work? Identify similar items Identify similar users Collaborative filtering (item based – user based) Ioannis Katakis, Social Recommendations in VAAs 5
    6. 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.) Ioannis Katakis, Social Recommendations in VAAs 6
    7. 7. Classification Algorithms Decision Trees Bayesian Ioannis Katakis, Social Recommendations in VAAs Neural Networks Support Vector Machines 7
    8. 8. Data Clustering – Unsupervised Learning Identify groups of similar items Similarity?  Euclidean Distance Algorithms?  k-Means, EM, etc. Ioannis Katakis, Social Recommendations in VAAs 8
    9. 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 Ioannis Katakis, Social Recommendations in VAAs 9
    10. 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)% Ioannis Katakis, Social Recommendations in VAAs 10
    11. 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 Ioannis Katakis, Social Recommendations in VAAs 11
    12. 12. Approaches Clustering Collaborative Filtering ? k-nearest Neighbors Ioannis Katakis, Social Recommendations in VAAs Classifiers  Neural Network  Support Vector Machine  Naïve Bayes  Decision Tree 12
    13. 13. Results – basic approaches Social Approaches > Party Coding Ioannis Katakis, Social Recommendations in VAAs Data: Greece, 2011 13
    14. 14. Results – various classifiers Support Vector Machines – Best Predictive Performance Collaborative Filtering - Fast + Accurate Ioannis Katakis, Social Recommendations in VAAs 14
    15. 15. Results – various datasets Party-Coding < SMO in all datasets  Difference between datasets maybe correlated with number of parties, training data size, community agreement Ioannis Katakis, Social Recommendations in VAAs 15
    16. 16. … in the VAA Also in… Cyprus 2013, Germany 2013, … Ioannis Katakis, Social Recommendations in VAAs 16
    17. 17. What users think… Like button likes satisfaction = likes + dislikes + neutral Users seem to like more the social recommendations Ioannis Katakis, Social Recommendations in VAAs 17
    18. 18. What else? – Attribute Selection Information Gain: ΙG D, a = H D − H T a H : information entropy Ioannis Katakis, Social Recommendations in VAAs 18
    19. 19. What else? – Data Clustering Ioannis Katakis, Social Recommendations in VAAs 19
    20. 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 Ioannis Katakis, Social Recommendations in VAAs 20
    21. 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 Ioannis Katakis, Social Recommendations in VAAs 21

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