a mobile linguistic decision support system to help … · masses and the collective intelligence,...

A MOBILE LINGUISTIC DECISION SUPPORT SYSTEM TOHELP USERS IN THEIR E-COMMERCE ACTIVITIES

I.J. Perez1 S. Alonso2 F.J. Cabrerizo3 E. Herrera-Viedma1

1 Dept. of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain

{ijperez,viedma }@decsai.ugr.es2 Dept. of Software Engineering, University of Granada, 18071 Granada, Spain

[email protected] Dept. of Software Engineering and Computer Systems, Distance Learning University of Spain (UNED), 28040 - Madrid, Spain

[email protected]

Abstract

With the recent spreading of social networksand digital communities, the Web has a newbusiness model for providing free content-sharing services. Using the power of themasses and the collective intelligence, Web2.0 sites have created enormous e-commerceopportunities for marketing and advertising.Thus, there is still a necessity of developingtools to help users to reach good decisions.We propose to manage this collective intel-ligence with a Mobile Decision Support Sys-tem (MDSS). It allows that users with thesame user profile that the potential customercan act as a set of shop assistants that ad-vices him/her according with their own ex-periences. Then, the MDSS obtains a col-lective advice that helps customers to makedecisions related with their e-commerce ac-tivities.

Keywords: Group decision making, e-commerce, decision support system, linguis-tic approach, web 2.0, social network, collec-tive intelligence.

1 INTRODUCTION

The global build of Internet connectivity and growingavailability of computing and communication deviceshave made the World Wide Web a new virtual border-less continent. New Web technologies have allowed thecreation of many different services where users fromall over the world can join, interact and produce newcontents and resources. One of the most recent trends,the so called Web 2.0 [14], represents a paradigm shiftin how people use the web which comprises a set of

different web development and design techniques. Itallows the easy communication, information sharing,interoperatibility and collaboration in this new virtualenvironment. Web 2.0 Communities, that can takedifferent forms as Internet forums, groups of blogs, so-cial network services and so on, provide a platform inwhich users can collectively contribute to a Web pres-ence and generate massive content behind their virtualcollaboration [12].

Due to the above new Web features, we can thinkabout great potentials and challenges for the futureof some internet business like electronic commerce (e-commerce). E-commerce is the buying and sellingof goods and services on the Internet, and it is nowspreading into all walks of life. Even, users can view,select and pay for online services in a mobile frame-work [1, 12]. New challenges about e-commerce in-volves new technologies, services and business mod-els. Social shopping sites emerged as the latest de-velopments to leverage the power of social networkingwith online shopping. Users on social shopping sitescan post product recommendations, create wish lists,post photos, make purchases, and form social shoppingcommunities [12].

We propose to incorporate new services (web 2.0) andtechnologies (mobile devices) in a decision support sys-tem (DSS) for advising customers in their e-commerceexperiences on-time. The central goal of DSSs is toprocess and provide suitable information in order tosupport individuals or organizations in their decisionmaking tasks like to decide where travel in holidaysor shopping elections [4]. Usually, people bring theirmobile devices with them anywhere. So, they havethe possibility to use some mobile services whereverthey go. Studies of mobile commerce suggest thatthere is a general customer interest in the services thatit provides, like shopping and banking applications[3, 11, 15]. Its adoption, however, has been slowerthan expected. Mobile phones impose very differentconstraints than desktop computers. It has been ar-

ESTYLF 2010, Huelva, 3 a 5 de febrero de 2010

XV Congreso Español Sobre Tecnologías y Lógica Fuzzy 575

gued that this stems from complexity of the transac-tions, perceived lack of security, lack of user friendlymobile portals, etc. [7, 15].

Due to the big amount of different items and onlineadvertisements, the first problem for the customer is torank the different alternatives in order to buy the bestone taking into account his/her connected partners’advice. This problem can be seen as a group decisionmaking (GDM) problem because it includes all thenecessary requirements for this kind of problems.

GDM models are used to obtain the best solution(s)for a problem according to the information provided bysome decision makers (expert). Usually, each expertmay approach the decision process from a different an-gle, but they have a common interest in reaching anagreement on taking the best decision. Concretely, ina GDM problem we have a set of different alternativesto solve the problem and a set of experts which areusually required to provide their preferences about thealternatives by means of a particular preference format[9]. In the case of e-commerce advising, the alterna-tives are the items or services that can be bought bythe customer, and the set of experts of the problemare the members connected with the customer’s socialnetwork.

To deal with the above situations, we present a mobileDSS that could be incorporated as a tool into a socialnetwork to help customers in their e-commerce activ-ities. To advise customers in their electronic shop-ping, the MDSS shows to the customer the rankingextracted by aggregating the collective intelligence ofthe virtual community. In such a way, our system al-lows that the members connected with the customerhelp him/her to choose the best good or service ofthe stock, according to the customer’s needs. To rep-resent the preferences provided by the social networkmembers we use a fuzzy linguistic modelling [18].Thus,to compute the quality assessments we use computingwith words tools based on linguistic aggregation op-erators [10] and a estimation algorithm to deal withincomplete information [2].

To do so, the paper is set as follows: in section 2 wepresent our preliminaries, that is, some of the mostimportant characteristics of Web 2.0 Communities andthe basic concepts that we use in our paper like GDMproblems and computing with words. Section 3 dealswith the incorporation of the MDSS as a mobile web2.0 service. Finally, in section 4 we point out our con-clusions.

2 PRELIMINARIES

2.1 New E-Commerce Opportunities Basedon Web 2.0

Emerging web and mobile technologies provide thefoundation for new e-commerce opportunities and ap-plications. They also generate the push for new enter-prise system architectures in order to deploy new Webtechnologies and transform businesses into e-commercedriven operations. This new framework allows peo-ple from all over the globe to meet other individu-als which share some of their interests. Thus, vir-tual communities can be created in order to collabo-rate, communicate, share information or resources andso on. The Web has become a media supported byadvertisers that, in turn, sell their products to usersvia e-commerce. In digital cyberspace, e-commerce ismoving into a new border, mixing physical and digi-tal goods trading. Companies have even created theirown digital currencies for online exchange of goods andservices [1, 12, 13].

E-commerce is an area of study that has practicalapplications and a prominent future. Although theInternet bubble has generated some negative senti-ment about the industry, many previously unsuccess-ful e-commerce ventures and business models have nowreemerged and become feasible and profitable. Withubiquitous broadband connectivity and powerful per-sonal devices (including PCs, mobile phones, and me-dia players), mobile commerce has become a grow-ing part of our daily lives. More important, endlessopportunities still exist to deploy new mobile com-merce products and services that are simply impossi-ble without automated Web-based and mobile support[1, 12, 13].

With most online shopping, people have numerouschoices when looking for a specific item in which theyare interested. Many shoppers use search engines butoften get more information than what they really want.In this paper, we want to identify an unexplored mar-ket and to create new products and services usingmobile tools and collective intelligence to help cus-tomers. To do so, we propose a linguistic mobile de-cision support system that acts like a recommendersystem where the recommendations are given by otherusers of the social networking web site who have thesame user profile that the customer. This applicationcould improve the customers’ satisfaction in their e-commerce activities.

2.2 Group Decision Making Models

A decision making process, consisting in deriving thebest option from a feasible set, is present in just about


576 XV Congreso Español Sobre Tecnologías y Lógica Fuzzy

every conceivable human task. It is obvious that thecomparison of different actions according to their de-sirability in decision problems, in many cases, cannotbe done by using a single criterion or an unique per-son. Thus, we interpret the decision process in theframework of group decision making (GDM).

There have been several efforts in the specialized lit-erature to create different models to correctly addressand solve GDM situations. Some of them make useof fuzzy theory as it is a good tool to model and dealwith vague or imprecise opinions. Many of those mod-els are usually focused on solving GDM situations inwhich a particular issue or difficulty is present. Forexample, there have been models that allow to uselinguistic assessments instead of numerical ones, thusmaking it easier for the experts to express their pref-erences about the alternatives [9]. Other models allowexperts to use multiple preference structures [5] andother different approaches deal with incomplete infor-mation situations if experts are not able to provide alltheir preferences when solving a GDM problem [2].

In a GDM problem we have a finite set of feasiblealternatives. X = {x1, x2, . . . , xn}, (n ≥ 2) andthe best alternative from X has to be identified ac-cording to the information given by a set of experts,E = {e1, e2, . . . , em}, (m ≥ 2).

Usual resolution methods for GDM problems are com-posed by two different processes [9] i) consensus pro-cess and ii) Selection process. (see Figure 1):

Figure 1: Resolution process of a GDM

In this paper we center our attention only in the se-lection process implementation, where the system hasto obtain the solution ranking of alternatives from theopinions on the alternatives given by the experts.

2.3 Use of Incomplete Linguistic Informationin GDM Problems

There are some situations in which the informationmay be not quantified due to its nature, and thus, itmay be stated only in linguistic terms. Linguistic de-

cision analysis is applied for solving decision makingproblems under linguistic information. Its applicationin the development of the theory and method in deci-sion analysis is very beneficial because it introduces amore flexible framework which allows us to representthe information in a more direct and adequate waywhen we are unable to express it precisely. So, thismetod has benn widely used in decision field [2].

The ordinal fuzzy linguistic modelling [10] is a usefulkind of fuzzy linguistic approach proposed as an alter-native tool to the traditional fuzzy linguistic modelling[18]. This tool simplifies the computing with wordsprocess as well as some linguistic aspects of problems.It is defined by considering a finite and totally orderedlabel set S = {si}, i ∈ {0, ..., g} in the usual sense, i.e.,si ≥ sj if i ≥ j, and with odd cardinality (usually 7 or9 labels).

Other different issue arises when each expert hashis/her own experience concerning the problem beingstudied and they could have some difficulties in givingall their preferences. This may be due to an expert notpossessing a precise or sufficient level of knowledge ofthe problem, or because that expert is unable to dis-criminate the degree to which some options are betterthan others. In such situations, experts are forced toprovide incomplete fuzzy linguistic preference relations(FLPRs) [2, 16]. Therefore, it is of great importanceto provide tools to deal with this lack of knowledge inexperts’ opinions.

We assume that each social network member eh pro-vides his/her preferences by means of a FLPR Ph

which, depending on the situation, could be incom-plete. So, before to start the selection process, wehave to fill the gaps of all the missing values by us-ing an additive consistency based estimation processdeveloped in [2].

On the other hand, an useful linguistic aggregationoperator is the Linguistic Ordered Weighted Averaging(LOWA) operator which has been extensively used inthe literature by its good axiomatic properties [10].We shall use it in our MDSS.

3 A MOBILE LINGUISTICDECISION SUPPORT SYSTEMTO HELP USERS IN THEIRE-COMMERCE ACTIVITIES

To develop the MDSS as a Web 2.0 service, the sys-tem asks customer his/her current needs (the tool of-fers a personalized service). Taking into account theseneeds, together with the community’s collective knowl-edge, the system shows to the customer the rankingthrough his own mobile device. Therefore, the cus-



tomer receives a social support to choose the best itemfor buying. The advise is represented by means of lin-guistic rankings of shopping alternatives obtained fromthe individual preference relations provided by the so-cial network members applying a selection process.

To do so, we have chosen a classical “Client/Server”architecture, where the client is a mobile device. Whatis more, as we want to develop the MDSS like a webservice, our implementation has thin client featuressuch as no additional technology investment required,and no risk of client-side software becoming obsolete.There is no need for additional servers, and no uniqueclient-side software or upgrade costs. Its main draw-back is that all content cannot be easily delivered toall browsers.

In what follows, we describe the client interface andthe server structure in detail.

3.1 Client Interface

The client software shows a specific interfaz to theusers depending on the kind of user. At first, the userhas to introduce his/her password in the system, then,the system decides the kind of interfaz to show. Thereare two different interfaces: Customer interface andMember Interface.

1. Customer interface: This interface is designedfor the customers that need help with their m-commerce experiences.

• Select alternatives: To introduce the pre-ferred items to buy (see Figure 2 a).

• Needs survey: To choose the importance de-gree of the item’s features (see Figure 2 b).

• Output: At the end of the decision process,the device will show the set of solution al-ternatives as an ordered set of alternativesmarking the most relevant ones (see Figure 3a).

2. Member Interface: This interface is designed forthe social network members that can help the po-tential customers.

• Problem description: The device shows tothe members a brief description about theproblem and the discussion subset of alter-natives.

• Insertion of preferences: The device has aspecific interface to insert the linguistic pref-erences using a set of labels. To introducethe preferences on the interface, the user hasto use the keys of the device (see Figure 3 b).

Figure 2: a) Selection of alternatives. b) Needs survey

Figure 3: b) Displayed ranking advice. b) Insertion ofindividual advices

3.2 Server Structure

The web server is the main side of the MDSS. It imple-ments the estimation process, the selection process andthe database that stores not only the problem data,but also problem parameters and some informationgenerated during the decision process. The commu-nication with the client to receive/send informationfrom/to the experts is supported by mobile Internet(M-Internet) technologies.

Once the customer has submitted his/her preferreditems and his/her current needs, the members con-nected with him/her have to give their opinions, takinginto account the customer’s needs, about the alterna-tives that the customer has selected (see Figure 2).When all the experts have given their opinions usingincomplete FPLRs as element of preferences’ repre-sentation, the system starts to compute the collectiveadvice.



3.2.1 Estimation Process

In [2] we develop an additive consistency based estima-tion process of missing values to deal with incompleteFLPRs defined in a 2-tuple linguistic context. In thispaper we adapt it to deal with incomplete FLPRs de-fined in an ordinal linguistic context.

To deal with incomplete FLPRs we need to define thefollowing sets [16]:

A = {(i, j) | i, j ∈ {1, . . . , n} ∧ i 6= j}MV h = {(i, j) ∈ A | ph

ij is unknown}EV h = A \MV h

(1)

where MV h is the set of pairs of alternatives whosepreference degrees are not given by expert eh and EV h

is the set of pairs of alternatives whose preference de-grees are given by the expert eh. We do not take intoaccount the preference value of one alternative overitself as this is always assumed to be equal to sg/2.

The subset of missing values that could be estimated instep t of the process, called EMV h

t (estimated missingvalues), is defined in [2].

When EMV hmaxIter = ∅, with maxIter > 0, the

procedure will stop as there will not be any moremissing values to be estimated. Furthermore, if⋃maxIter

l=0 EMV hl = MV h, then all missing values are

estimated, and, consequently, the procedure is saidto be successful in the completion of the incompleteFLPR.

In iteration t, to estimate a particular value phik with

(i, k) ∈ EMV ht , we use the function estimate p(h, i, k)

[2].

Then, the complete iterative estimation procedure isthe following:

ITERATIVE ESTIMATION PROCEDURE

0. EMV h0 = ∅

1. t = 12. while EMV h

t 6= ∅ {3. for every (i, k) ∈ EMV h

t {4. estimate p(h,i,k)5. }6. t + +7. }

3.2.2 Selection Process

The selection process implemented computes all theindividual preferences to obtain a collective advice. Ithas two different phases: Aggregation and Exploita-tion [8]:

1. Aggregation:

The aggregation phase defines a collective prefer-ence relation, P c =

(pc

ij

), obtained by means of

the aggregation of all individual linguistic prefer-ence relations

{P 1, P 2, . . . , Pm

}. It indicates the

global preference between every pair of items ac-cording to the majority of experts’ opinions. Theaggregation is carried out by means of a LOWAoperator [6, 10].

A LOWA operator of dimension n is a functionφ : Sn → S that has a weighting vector associ-ated with it, W = (w1, ..., wn), with wi ∈ [0, 1],∑n

i=1 wi = 1, and it is defined according to thefollowing expression:

φQ(p1, . . . , pn) =n∑

i=1

wi · pσ(i), pi ∈ S.

being σ : {1, ..., n} → {1, ..., n} a permutationdefined on linguistic values, such that pσ(i) ≥pσ(i+1),∀i = 1, ..., n − 1, that is, pσ(i), is the i-highest linguistic value in the set{p1, ..., pn}.A natural question in the definition of OWA op-erators is how to obtain W . In [17] it was definedan expression to obtain W that allows to repre-sent the concept of fuzzy majority by means of afuzzy linguistic non-decreasing quantifier Q:

wi = Q(i/n)−Q((i− 1)/n), i = 1, . . . , n. (2)

Therefore, in our model the collective FLPR isobtained as follows:

pcij = φQ(p1

ij , . . . , pmij ) (3)

We can use the linguistic quantifier most of de-fined as Q(r) = r1/2, that generates a weightingvector of n values. As an example with three ex-perts, W = (0.58, 0.24, 0.18). To obtain each col-lective ordinal linguistic preference value pc

ij wehave to do that follows:

• p1ij = M, p2

ij = L, p3ij = V L ⇒ σ(1) =

1, σ(2) = 3, σ(3) = 2• pc

ij = Γ(w1 · Γ−1(p1ij) + w2 · Γ−1(p3

ij) + w3 ·Γ−1(p2

ij)) = Γ(0. 58 · 3 + 0. 24 · 1 + 0. 18 · 2) =Γ(2. 34) = L

2. Exploitation:

This phase transforms the global informationabout the alternatives into a global ranking ofthem, from which the set of solution alternativesis obtained. The global ranking is obtained apply-ing two choice degrees of alternatives on the col-lective preference relation QGDD and QGNDD.These degrees can be studies in more detail in [2].



When the system has computed all these values, thecustomer receives them in his mobile device in form ofadvice (see Figure 3 a):

4 CONCLUSIONS

We have presented a MDSS tool based on GDM mod-els as a Web 2.0 service related with e-commerce andcollective intelligence that is represented with incom-plete FLPRs. This tool uses the advantages of M-Internet communication technologies to advise the cus-tomer in their m-commerce experiences and improvesthe customer satisfaction with the decision of pur-chase in anytime and anywhere. We have used smart-phones as device to send the experts’ preferences butthe structure of the prototype is designed to use anymobile device as PDAs.

Acknowledgements

The word Acknowledgements This paper hasbeen developed with FEDER funds, PETRI project(PET2007-0460) and FUZZYLING project (TIN2007-61079).

References

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