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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 for eGovernment
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 for eGovernment
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
Recommendation Approach
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
Recommendation Approach
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).
Recommendation Approach
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:
Recommendation Approach
30
X2
X1
X3Y1
Y2
Sammon Mapping
• Shammon mapping from a three-dimensional space to a bi-dimensional space.
Recommendation Approach
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
Recommendation Approach
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:
Recommendation Approach
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