a fuzzy recommender systems for eelections - unifr.ch · collaborative filtering: based on a...

A Fuzzy Recommender Systems for eElections

Information Systems Research GroupUniversity of Fribourg

Luis Terán and Andreas Meier

EGOVIS 2010 Bilbao, SpainUniversity of DEUSTO

30 August - 3 September 2010

Outline

• Motivation

• Recommender Systems

• Smartvote System

• Fuzzy Clustering

• Recommendation Approach

• Recommendation Output

• Conclusions

• References

2

Election: is a formal decision-making process by which a population chooses an individual to hold a public office (British Encyclopedia).

Citizens

3

Candidates

Few candidates (Presidential Elections)

Election

Motivation

Election: is a formal decision-making process by which a population chooses an individual to hold a public office (British Encyclopedia).

Citizens

4

Candidates

Many candidates (General Elections)

Election

Motivation

5

Motivation

Problems and Questions:• Too many candidates• Too many political parties• Which candidates better represents the Voter?• How to make the best decision?

6

In [4], Yager makes a distinction between Recommender Systems and Targeted Marketing.

• Recommender Systems are participatory systems, where users intentionally provide information about their preferences.

• Targeted Marketing methods are based on extensional information, which refers actions or past experiences on specific objects.

Recommender Systems

The more widely used techniques in recommender systems are based on Collaborative Filtering (CF) methods [1].

Item A?

Item B

Item A Item A

Recommender Systems

7

RS are computer-based techniques used to reduce information overload and to provide recommendations of products likely to interest a user given some information about the user’s profile.

Recommender Systems for eCommerce

Item 1 Item 2 Item3 Item 4 Item 5 Item 6 Item 7

User 1

User 2

User 3

User 4

User 5

5 3 4 1 0 0 0

5 3 4 1 5 2 5

5 0 4 1 5 3 0

1 3 2 5 1 4 2

4 0 4 4 4 0 4

Collaborative Filtering: based on a User-Item Matrix of Rankings

Objectives• Estimate missing rankings (prediction)• Recommend items with bigger ratings (recommendation)

8

Recommender Systems for eCommerce

Recommender Systems

0 -> no ranking.1-5 -> rank.

Recommender Systems for eGovernment aim to solve problems of information overload on eGovernment services, which could help to improve the interaction between public administrations, citizens and the private sector.

Citizens

Internet

Government

Business

9

Recommender Systems for eGovernment

Recommender Systems

eGovernment Framework of the Information Systems Research Group.• Level I: design of eGovernment portals (web 2.0)• Level II: eProcurement, eService (taxation, registration, etc.), eContracting,

and eSettlement.• Level III: participation.

LEVEL IIIParticipation

LEVEL IIProduction

LEVEL IInformation &

Communication

eCollaboration eDemocracy eCommunity

eProcurement eService eContracting eSettement

eAssistance

Knowledge Society

eDemocrac

Level III: participation.

10

Copyright 2008 University of Fribourg


Recommender Systems

eDiscussion

eVoting

eElection

ePosting

• Special topics• Decision aids• Forums• Subscription services

• Publication of results• Visualization options• Evaluation of results• Participation in blogs

eDemocracy:

eDiscussion: citizens can know more about the candidates or the subject in a voting process.ePosting: gives the possibility to open discussion channels (Political Control and Public Memory)

Reco. Sys. Reco. Sys.

11


Recommender Systems

Online Voting Advice Application (VAA) for communal, cantonal and national elections in Switzerland based on profile comparison between candidates and voters

12

Smartvote

• Welfare, Family and Health

• Education and Sport

• Migration and Integration

• Society, Culture and Ethics

• Finances and Taxes

• Economy and Work

• Environment, Transport and Energy

• State Institutions and Political Rights

• Justice and Order

• Foreign Policy and Foreign Trade

• Fields of Activity

13

Voter/Candidate Profile -> Questionnaire (30 - 70 questions)

Smartvote

14

Smartvote

Tendency Relevance

Recommendation based on computation of “Match Points”

Bonus

15

Smartvote

• Match between voter and candidate

• Matching in percentage

16

SmartvoteWi is only for

voter

• Recommendation by Full List -> mean average of candidates in the list

• Consider relevance (“-”, “=”, “+” -> 0.5, 1, 2)

17

SmartvoteOutput

18

SmartvoteOutput

19

SmartvoteOutput

20

SmartvoteOutput

Sharp Clustering: each element is associated just to one cluster.

21

Fuzzy Clustering

middle-age oldyoung

Mem

bers

hip

func

tion

Age

1

0 t1

μyoung(t1) = 1

t

middle-age oldyoung

Mem

bers

hip

func

tion

Age

1

0 t1

μyoung(t1) = 0.7

μmiddle(t1) = 0

μold(t1) = 0

t

μmiddle(t1) = 0.3

μold(t1) = 0

middle-age oldyoungM

embe

rshi

p fu

nctio

n

Age

1

0 t1

μyoung(t1) = 1

t

middle-age oldyoung

Mem

bers

hip

func

tion

Age

1

0 t1

μyoung(t1) = 0.7

μmiddle(t1) = 0

μold(t1) = 0

t

μmiddle(t1) = 0.3

μold(t1) = 0Fuzzy Clustering: unsupervised learning task which aims to decompose a set of objects into “clusters” based on similarities, where the objects belonging to the same cluster are as similar as possible.

Fuzzy c-means algorithm • Based on c-means algorithm• Defines:

Samples ->

Clusters ->

Partition Matrix ->

Membership degree ->

• Constrains:

Guarantee that clusters are not empty, and that the sum of the membership for each x is equal to 1

22

Fuzzy Clustering

where: m -> level of fuzziness (normally m=2, and if m=1 -> sharp clustering) yi -> d-dimensional center of cluster i

Taking derivative of Jm with respect to the parameters to optimize equal to zero we get:

23

Fuzzy ClusteringFuzzy c-means algorithm • Based on minimization of an objective function:

24

Fuzzy ClusteringFuzzy c-means algorithm • Finally, FCM algorithm is a two-step iterative process defined as

follows:

Fuzzy Recommender System Architecture• The recommendation process is given in three steps:

Fuzzy Interface

Voter/Candidate

Database

! Fuzzy Profile Generation

RecommendationEngine

" Ask for Recommendation

# Receive

Recommendation.

Top-N Recommedantion

Fuzzy cluster

Fuzzy Clustering

FUZZY RECOMMENDER SYSTEM

25

Recommendation Approach

1. Fuzzy Profile Generation 2. Ask for

Recommendation

3. Receive Recommendation

1. Fuzzy Profile Generation 2. Ask for

Recommendation

3. Receive Recommendation

Fuzzy Interface• It is convenient tool is used to determine the level of

agreement/disagreement and relevance for a specific question.

3 Should the protection provisions of wolves be relaxed?

No AnswerDisagree AgreeIndifferent - = +ENVIRONMENT, TRANSPORT ENERGY

Relevance

100 50 0 50 100 50 0 50 100100

3 Public Transport

No Answer

Spend Less

Spend More

Spend the same - = +FIELD OF ACTIVITY

Relevance

100 50 0 50 100 50 0 50 100100

26


Tendency Relevance

Recommendation Engine

• Transforms the high-dimensional space of FP to a bi-dimensional to reduces the complexity of data analysis using the Sammon Mapping [8].

• Sammon mapping tries to preserve inter-pattern distances.

27


Sammon Mapping

• Sammon mapping is based on a minimization of a “stress function E” defied as:

28

where:

dij are the distances between points xi and xj (original space), and d’ij are the distances between points yi and yj (mapped space).


Sammon Mapping

• Sammon applied a steepest descent technique, where the new at iteration is given by:

29

where:

is the l-th coordinate of point in the mapped space, α is a constant computed empirically to be α ≈ 0.3 or 0.4.

The partial derivatives are given by:


30

X2

X1

X3Y1

Y2

Sammon Mapping

• Shammon mapping from a three-dimensional space to a bi-dimensional space.


Fuzzy Cluster Analysis• FRS generates a fuzzy clusters using a modified fuzzy c-means• Prior knowledge of data is required• Assumptions:

- Number of clusters -> number of political parties- Initial center of clusters -> random member of each political party

31


32

Top-N Recommendation

• Distances of all candidates with respect to voter v• Top N candidates close to v are displayed.• Similarity in percentage is computed by:


33

Parameters:

• Fuzzy c-means: • Sammon mapping:

FRSP provides two graphical interfaces

• Fuzzy Cluster Analysis Graphical Interface (FCAGI)• Top-N Recommendation Graphical Interface (TNRGI)

First Experiments:

• Three Political Parties• Random Profile of Candidates• Random Profile of Voter

Recommendation Output

34

Recommendation OutputFuzzy Cluster Analysis

Voter

35

Recommendation OutputTop-N Recommendation

Voter

92%

89%

86%

81%

Last Experiment:

• Data set provided by Smartvote project [14] • Swiss National Elections 2007• Candidates from 3 Political Parties• Voter is selected from Smartvote database

36

Recommendation Output

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Fuzzy Cluster Analysis

Center SPCenter RDPCenter CDUVoterCentral Democratic UnionSocialist PartyRadical Democratic Party

37

Recommendation OutputFuzzy Cluster Analysis

Voter

Center: Radical Democratic Party

Center: Socialist Party

Center: Central Democratic Union

38

Recommendation OutputTop-N Recommendation

Voter

72%

73%

77.2%

77%

39

• FRS for eElections could increase participation, which can help to contribute with democratic processes

• FRS introduces a new tool -> Fuzzy Cluster Analysis• FRS is suitable in the one-and-only item scenario• FRS can be applied in other domain such as:

- Selling House- Trade Exhibitions- Community Building

• FRSP will be extended and tested with real data.

Conclusions

Questions?

40

[1] Vozalis, E., Margaritis, K.: Analysis of Recommender Systemsʼ Algorithms. The Sixth Hel- lenic European Conference on Computer Mathematics and its Applications (HERCMA 2003). Athens, Greece (2003)

[2] Guo, X., Lu J.: Intelligent E-Government Services with Personalized Recommendation Tech- niques. International Journal of Intelligent Systems. vol. 22, pp. 401–417 (2007)

[3] Sarwar, B., Karypis, G., Konstan, J.: Item-based Collaborative Filtering Recommendation Algorithms. 10th International World Wide Web Conference. pp. 285-295, Hong Kong (2001)

[4] Yager, R.: Fuzzy Logic Methods in Recommender Systems. Fuzzy Sets and Systems 136. pp. 133-149 (2003)[5] Mobashe, R., Burke, R., Sandvig, J.: Model-Based Collaborative Filtering as a Defense Against Profile Injection

Attacks. Proceedings of the 21st National Conference on Artificial Intelligence (AAAIʼ06). Boston, Massachusetts (2006)

[6] Zadeh, L.: Fuzzy Sets. Department of Electrical Engineering and Electronics Research Labo- ratory. Berkeley, California (1965)

[7] Bezdec, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press. New York (1981)[8] Sammon,J.W.:ANonlinearmappingforDataStructureAnalysis.IEEETransactionsonCom- puters, Vol. C-18, No. 5

(1969)[9] Meier, A.: eDemocracy & eGovernment. Springer, Berlin (2009)[13] Valente de Oliveira, J., Witold, P.: Advances in Fuzzy Clustering and its Aplications. Wiley, West Sussex (2007)[14] Smartvote, http://www.smartvote.ch/[15] ACE Project. http://aceproject.org/ace-en/topics/pc/pca/pca01/pca01a [16] European" Commission."http://ec.europa.eu/information_society/activities/egovernment/index_en.htm[17] Schwarz D., Schädel L., Ladner A.: Pre-Election Positions and Voting Behavior in Parliament: Explaining Positional Congruence and Changes among Swiss MPs. Swiss Political Science Review. 2010 (forthcoming).[18] Fivaz J. Felder G.: Added Value of e-Democracy Tools in Advanced Democracies? The Voting Advice Application smartvote in Switzerland. Shark, Alan R. and Sylviane Toporkoff (eds.). Beyond eGovernement–Measuring Performance: A Global Perspective. Washington, DC: Public Technology Institute and ITEMS International; pp. 109-122. (2009)

References

a fuzzy recommender systems for eelections - unifr.ch · collaborative filtering: based on a...

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