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  • A Fuzzy Recommender System for eElections

    Luis Teran and Andreas Meier

    Information Systems Research Group, University of Fribourg,Boulevard de Perolles 90, 1700 Fribourg, Switzerland


    Abstract. eDemocracy aims to increase participation of citizens in demo-cratic processes through the use of information and communication tech-nologies. In this paper, an architecture of recommender systems foreElections using fuzzy clustering methods is proposed. The objective isto assist voters in making decisions by providing information about can-didates close to the voters preferences and tendencies. The use of recom-mender systems for eGovernment is a research topic used to reduceinformation overload, which could help to improve democratic processes.

    Keywords: Recommender Systems, eDemocracy, eElections, Fuzzy Logic,Fuzzy Clustering.

    1 Introduction

    Recommender systems are computer-based techniques used to reduce informa-tion overload and to provide recommendations of products likely to interest auser. This technique is mainly used in eCommerce to suggest items that a cus-tomer is assumably going to buy.

    In [4], Yager makes a distinction between recommender systems and tar-geted marketing. Yager considers that recommender systems are participatorysystems, where users intentionally provide information about their preferences.In contrast, targeted marketing methods are based on extensional information,which refers actions or past experiences on specific objects.

    The definition of recommender systems as introduced by Yager is used in thispaper. In addition, it is assumed that users are willing to provide informationabout their preferences. It is important to mention that recommender systemsfor eElections make no use of past actions where each event is consider as unique.

    The paper introduces a fuzzy-based architecture of a recommender systemfor eElections. First, a description of recommender systems for eCommerce andeGovernment is presented in Sect. 2 and 3. Sect. 4 gives a brief introduction tofuzzy logic, fuzzy sets, and fuzzy clustering methods used in the recommendationapproach. The fuzzy recommender system architecture is described in Sect. 5.Finally, the conclusions are drawn in Sect. 6.

    K.N. Andersen et al. (Eds.): EGOVIS 2010, LNCS 6267, pp. 6276, 2010.c Springer-Verlag Berlin Heidelberg 2010


  • A Fuzzy Recommender System for eElections 63

    2 Recommender Systems for eCommerce

    According to Yager [4], recommender systems used in eCommerce are targetedmarketing methods, which rely on past experiences to increase the sales of prod-ucts. The main problems, and the more widely used techniques of recommendersystems in eCommerce are described in this section for better understanding oftheir use and scope.

    In [1], Vozalis and Margaritis describe the challenges and problems of recom-mender systems used in eCommerce. The most relevant are the sparsity and firstrater problems. The sparsity problem refers to the number of recommendationsmade by customers. This problem occurs when the number of items rated issmall compared to the total number of items. First rater problem refers to aproduct which can be the subject of a recommendation only if another user haspreviously rated it.

    According to [1], the more widely used techniques in recommender systems arebased on Collaborative Filtering (CF) methods. The goal of CF is to recommendor to predict the benefit of a specific product based on previously ranked itemsby the user and the opinions of other users with similar likings.

    In a CF scenario there is a list of users U = {u1, u2, . . . , um} and a list ofitems I = {i1, i2, . . . , in}. Each user has a list of items Iui for which other usershave provided their opinions (rating score). There exists a user ua U calledthe active user from whom the recommendation or prediction is computed. Amatrix R (m n user-item matrix), where each entry rij represents the ratingsof user ui about a specific item ij . The rating is a numerical value from 0 to amaximum value (0 value means no ranking).

    According to [2] and [3], the more widely used techniques in recommendersystems based on CF methods are: Memory-based (user-based), and Model-based(item-based). These techniques use a calculation of similarities between individ-uals (Memory-based) or items (Model-based).

    2.1 Memory-Based Collaborative Filtering Algorithms

    Memory-based algorithms compute neighborhood formations using the user-itemmatrix R, which contains the ratings of items by user. Nevertheless, users haveno obligation to provide their opinion on all items.

    In order to compute the similarity between users ui and uk from R, two meth-ods are more widely used: Pearson Correlation Similarity and Cosine/VectorSimilarity.

    Using the Pearson Correlation Similarity or Cosine/Vector Similarity, a mmsimilarity matrix S is generated including the similarity values between users.Using S, neighborhood formation is computed based on several schemas. Themost common according to [1] are: Centered-based Schema and Aggregate Neigh-borhood Schema.

    The final step in the recommendation process is to generate either predictionor a Top-N recommendation. Prediction Paj is a numerical value that expresses

  • 64 L. Teran and A. Meier

    the possible opinion of active user ua about an item ij for which the active userhas yielded no opinion. Top-N recommendation is a list of N items that theactive user ua would like the most.

    2.2 Model-Based Collaborative Filtering Algorithms

    In the model-based CF algorithm, a set of items ranked by the active user ua isused to compute similarities between these items and a target item ij to selectthe n most similar items. The first step in the computation of similarities betweenitems ii and ij is to isolate users that rated both items.

    The second step is the computation of similarities between items ii and ij .The main techniques used to compute the similarity simij according to [3] areCosine/Vector Similarity, Pearson Correlation, and Adjusted Cosine Similarity.After the computation of the similarities of items, the computation of the n mostsimilar items to a target item ij from its neighborhood N of items is performed.

    According to [1], the more widely used technique to compute predictions is theWeighted Sum method. It generates a prediction of item ij for an active user ua.

    Recommender systems used in eCommerce can be classified as Targeted Mar-keting, according to [4], since they use the past experiences of users. In thisapproach, the accuracy of the recommendation depends directly on the partic-ipation of users. These type of recommender systems are only suitable for therepeat-appeared items mentioned in [2], where a user can purchase an item eventhough some have been sold.

    3 Recommender Systems for eGovernment

    In [13], the term eGovernment refers to the use of information technologies toimprove the interaction between public administrations, citizens, and the privatesector. Three types of relationships are defined for eGovernment: Administra-tion to citizens (A2C), Administration to Business (A2B), and Administrationto Administration (A2A). In [9], Meier introduces an eGovernment frameworkconsisting of three levels: Information and Communications, Production, andParticipation. It is shown in Fig. 1.

    The lowest level provides information and communication for eGovernment;it focuses on the design of communal web portals. The second level consistsof the actual public services (electronic procurement, taxation, and electronicpayments, among others). The third level refers to citizen participation. Thispaper focuses on the participation level, specifically on eDemocracy.

    In [9], Meier mentions the importance of citizen participation in eDemocracy(eElection and eVoting). Meier defines eDiscussion as a stage where citizenscould know more about the candidates or the subject in a voting process. It usesinformation and communication technologies such as forums, decision aids, andsubscription services, among others, to aid voters in making decisions. In thesame way, once an eVoting or eElection process has been completed, anotherstage defined as ePosting is required [9]. This stage facilitates the publication of

  • A Fuzzy Recommender System for eElections 65

    LEVEL IIIParticipation

    LEVEL IIProduction

    LEVEL IInformation &


    eCollaboration eDemocracy eCommunity

    eProcurement eService eContracting eSettement


    Knowledge Society

    Fig. 1. eGovernment Process Oriented Maturity Model

    results, and it gives voters the possibility to open discussion channels about theprocess. Fig. 2 shows the stages of eVoting and eElection as part of a processchain.

    This paper proposes a fuzzy recommender system for eElections used by vot-ers to establish the most similar candidates according to their preferences andtendencies. In addition, it is used after a voting/election process to establish thepreferences and tendency of the new elected authorities.





    Special topics Decision aids Forums Subscription services

    Publication of results Visualization options Evaluation of results Participation in blogs

    Fig. 2. Stages of eVoting and eElection as part of a Process Chain

    According to Yager in [4], and assuming that users are willing to collab-orate in providing information about their preferences, recommender systemsused in eGovernment are classified as Participatory systems. These types ofrecommender systems are suitable for the one-and-only item [2], where the rec-ommendation target is a unique item/event. Examples of one-and-only items areselling a house, trade exhibitions, elections, and voting, among others, where therecommendation makes no use of their past actions since these events occur onlyonce.

    3.1 Smartvote

    Smartvote [11] is an online recommendation system for communal, cantonal andnational elections in Switzerland. Smartvote system is based on profile com-parison between candidates and voters. Smartvote recommendation is based onsimilarities between voters and candidates.

  • 66 L. Teran and A. Meier

    In order to compute their similarities, voters and candidates must create theirprofiles using a questionnaire of 30 to 70 questions about values, attitudes andpolitical issues. Smartvote questionnaire is based on two main types of questions:standard questions and budget questions. Standard questions are related to theacceptance or rejection of specific political issues, and budget questions intendto establish whether voters would spend more or less in certain areas.

    Smartvote questionnaire is composed of eleven categories: 1) welfare, famillyand health, 2) education and research, 3) migration and integration, 4) society,culture and ethics, 5) finances and taxes, 6) economy and work, 7) environment,transport and energy, 8) state institutions and political rights, 9) justice, policeand army, 10) foreign policy and foreign trade, and 11) fields of activity. Eachcategory contains different questions related to that topic. Those questions couldbe either standard questions or budget questions (For more information aboutSmartvote questionnaire refer to [11]).

    In order to generate the recommendations, Smartvote computes the MatchPoints using (1).

    MPi(v, c) = 100 |aiv aic| + b (1)where aiv and aic are the answers of voter v and candidate c to questions irespectively, MPi(v, c) represents the number of points of agreement (MatchPoints) of voter v and candidate c to questions i, and b represents a bonuswhich applies if and only if there is a perfect match (Yes-Yes and No-Nocombinations). Table 1 shows the values given in standard questions (similarprocedure is used for budget questions).

    Table 1. Points of agreement on Standard Questions

    V() C() Yes Prob. Yes Prob. No No TotalYes 100+50 75 25 0 250

    Prob. Yes 75 100 50 25 250

    Prob. No 25 50 100 75 250

    No 0 25 75 100+50 250

    Total 250 250 250 250

    The next step in the calculation of matching is to take into account the rele-vance of each question to voters. For each question there is selection by weighting+, =, and -, the corresponding match points are multiplied by the factors0.5, 1 and 2, as shown in (2)

    MPi(v, c, W ) = MPi(v, c) Wi (2)where MPi(v, c, W ) is the weight scored match of voter v and candidate c toquestion i.

  • A Fuzzy Recommender System for eElections 67

    Finally, (3) is used to compute the match between a voter v and candidate cto the n questions:

    MP (v, c, W ) =n


    MPi(v, c, W ) (3)

    Additionally, a matching value as percentage between voter v and candidate cis calculated using (4) and (5).

    MP (v)Max =n


    avi Wi (4)

    MP (v, c, W ) =MP (v, c, W )MP (v)Max

    100 (5)

    where MP (v)Max is the theoretical maximum possible match score dependingonly on the answer and weights of voter v, and MP (v, c, W ) represents thematching value as percentage between voter v and candidate c.

    The computation of Matching Point depends on the maximum possible matchscore which, in turn, depends on the answer given by voters. Smartvote can beused to generate recommendations by full lists, in this case the matching valuesare computed using the mean average of all candidates in the list.

    4 Fuzzy Logic, Fuzzy Sets and Fuzzy Clustering

    Fuzzy Logic is a multi-value logic which allows a better understanding of theresult of a statement more approximate than precise in real life. In contrast withsharp logic, where results of a statement are binary (true or false, one orzero), fuzzy logic admits a set of truth values in the interval [0, 1]. Fuzzy logicis derived from fuzzy set theory introduced by Zadeh in [6], where a fuzzy set isdetermined by a membership function with a range of values between 0 and 1.In [6], Zadeh gives a definition of fuzzy sets, shown below:

    Definition 1. A fuzzy set is built from a reference set called universe of dis-course. The reference set is never fuzzy. Assuming U = {x1, x2, ..., xn} as theuniverse of discourse, then a fuzzy set A (A U) is defined as a set of orderedpairs:

    {(xi, A(xi))}where xi U, A : U [0, 1] is the membership function of A and A(x) [0, 1]is the degree of membership of x in A.

    Fig. 3 shows an example of three fuzzy sets with the expressions young,middle-age, and old as function of the age of a citizen. young(t), middle(t),and old(t) are the membership functions for age t and the fuzzy sets. It is clearthat age t1 belongs to three sets with different degrees of membership.

    Fuzzy clustering is an unsupervised learning task which aims to decompose aset of objects into clusters based on similarities, where the objects belonging

  • 68 L. Teran and A. Meier

    middle-age oldyoung



    hip fu





    0 t1

    young(t1) = 0.7

    middle(t1) = 0.3

    old(t1) = 0


    Fig. 3. Example of Fuzzy Set Age

    to the same cluster are as similar as possible. In sharp clustering, each elementis associated to just one cluster.

    The main algorithms used to generate clusters are c-means (sharp clustering)and fuzzy c-means (fuzzy clustering). The fuzzy r...


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