flexible payment recommender system - rutgers …cimic.rutgers.edu/~soon/papers/jita2011.pdf ·...

Journal of Information Technology and Architecture

Vol. 8. No. 4, December 2011, Pages 299-316

299

Flexible Payment Recommender System

Soon Ae Chun1*, Yoo Jung An2, Sunju Park3, June-Suh Cho4 and James Geller5

1City University of New York – College of Staten Island2Fairleigh Dickinson University

3Yonsei University, Seoul, Korea4HanKook University of Foreign Studies, Seoul, Korea

5New Jersey Institute of Technology

(Received November 10, 2011; Revised November 25, 2011; Accepted December 3, 2011)

Abstract: When a customer owns multiple credit cards, the selection of a credit card for a purchase

payment is rarely done systematically. Furthermore, the customer has no online options to pay a high

cost item with multiple cards. This paper presents a flexible Payment Recommender (PR) system that

helps the customer to make a decision on identifying a group of credit cards, called Virtual Card, which

provides customers with the flexibility of customizing their payments by splitting a payment over

multiple cards. This intelligent PR system uses a divisible card payment decision model based on fuzzy

membership functions of the customer’s weighted preference parameters and suggests the best card or

best combination of cards to the customer. The PR system uses the Virtual Card Manager (VCM) that

modifies and extends the existing single credit card-based payment infrastructure, to support processing

multiple card-based flexible payments. A prototype PR system was implemented, that can be installed

on the customer’s PC or mobile device, to manage multiple credit cards and personal preferences, and

to support making payment decisions. A user acceptability study of the proposed PR system is also

reported.

Keywords: payment recommender system, divisible card payment decision model, flexible card,

virtual card, virtual card processing system, e-commerce, m-commerce

1. Introduction

A typical online payment involving a credit-card

payment consists of the following steps: the user

selects a credit card and charges the whole amount

for his/her purchase on the chosen card; the card

information is sent over a secure connection such as

SSL, and the credit is verified with the card provider.

In the case of a mobile payment with a credit card,

a mobile device acts as a credit card where a credit

card can be entered either directly to the device (e.g.

enter the credit number on the cell phone), or

through a card swipe or wireless card scanning with

the mobile device. Once the credit card information

is entered, the financial gateway interacts with the

card provider and verifies credit card authentication

and approval. The majority of the current electronic

commerce transactions or mobile payments are still

based on one credit card for one purchase.

However, a customer typically owns a set of credit

cards to choose from for payment. Each credit card

has certain features associated with it, such as inter-

est rates, credit limit, as well as some promotional

features such as cash-back on a percentage of total

purchases made, travel protection insurance, extend-

ed warranty on purchases, frequent flier miles, etc.

The customer is faced with making the decision on

which credit card to use for a particular transaction.

Typically, the customer’s choice of one credit card

over another (e.g. an Amex card over a Master card)

is arbitrary; for instance, she chooses one card over

another based on the first one at hand or based on

a habit. At best, her decision is made based on a

vague sense of the credit card provider’s reputation,

Soon Ae Chun, Yoo Jung An, Sunju Park, June-Suh Cho and James Geller

300 Journal of Information Technology and Architecture

remaining balance or interest rate, if she can keep

track of the features and remember them. In the cur-

rent payment scenarios, there is no automated sup-

port for a flexible payment system that recommends

the best card for each transaction that would opti-

mize her preferences and spending habits, consider-

ing all the combinations of criteria, benefits and

features provided by different cards.

Furthermore, the current credit card based online

payment methods lack flexibility that includes the

ability to split a purchase price over several cards,

instead of charging it on a single card. This flexibility

of using multiple cards for one payment is especially

beneficial for the customer. For example, a customer

may have a preferred card A with a low interest rate

and a second card B with a high interest rate. With-

out an option of splitting the purchase price over sev-

eral cards, the credit limit and prior spending on A

may force her to pay the purchase with the higher

interest rate card, B. The customer, however, would

be better off if she could first max out the remaining

credit limit on card A and then pay the balance of the

purchase price with card B, lowering her high inter-

est balance on card B. In other cases, a customer may

face a problem when paying for the purchase of a

major item, if its price is larger than the available

credit on each of her credit cards. While the total

available credit on all her cards may well exceed the

sale price, the merchant would not be able to com-

plete the sale, because today’s e-payment systems

can only accept a single credit card for a single trans-

action. In this case, it is in the best interest of the

merchant to allow the customer to split the payment

over multiple credit cards.

How to split a purchase price over multiple cards,

however, is a complex decision making problem [4].

Today's typical customer carries a wide variety of

cards with a corresponding variety of incentives,

such as frequent flyer miles, cash back, introductory

low interest rates, promotional gifts, etc. In addition,

some credit cards are issued through employer orga-

nizations, called secondary card issuers, with com-

pany specific policies, such as that the card is allowed

only for business expenses or for office supplies,

which will further affect the choice of cards. Fur-

thermore, sellers may impose their own constraints

such that not every credit card is accepted every-

where. Lastly, the choice of the ideal card (or com-

bination of cards) often depends on the paying habits

of the customer and the calendar date of the pur-

chase. Most customers would prefer to pay with a

credit card, which will buy them a delay. Thus, all

other factors being equal, a customer would prefer to

use a card where the closing date is 20 days away

over a card where the closing date is only 2 days

away. For customers who carry large balances over

long periods, the interest rate is an important deci-

sion factor. For the customers who pay their bills off

every month and do not care about interest rates at

all, on the other hand, the closing date or other incen-

tive offers might be more important parameters.

Therefore, while it is clearly in the interest of a

credit card user to be able to split a purchase price

over multiple cards, as described above, she may find

herself in a state of confusion about how to split the

payment exactly. We call this problem of choosing an

optimal card or card combination as the divisible

payment decision problem. Since the criteria for

choosing a set of credit cards for payment are diverse

and differ from one customer to another, customers

need help in keeping track of all their cards and their

incentives and constraints. In addition, at the time

of making a purchase, customers need help with the

complex decision of which card(s) to use and how to

optimally split a purchase over multiple cards if one

chooses to use multiple cards.

In this paper, we provide a flexible payment rec-

ommender system that solves the divisible payment

decision problem and proposes to the user the flex-

ible payment choice with the best combination of

cards for one purchase, based on the cards’ properties

such as promotional incentives, interest rates, as

well as users’ preferences. In order to accommodate

these flexible payment methods with multiple cards,

we propose a divisible card payment infrastructure

that modifies and extends the current credit card

payment verification infrastructure, often used in

electronic commerce. First, to support the divisible

card payments, the Virtual Card Manager (VCM) is

added to the merchant side. The VCM handles the


Journal of Information Technology and Architecture 301

divisible card approval process between the mer-

chant and the respective credit card issuers. Second,

to support the customer’s card-usage decisions, the

new infrastructure provides the customer with a

Payment Recommender agent (PR) that supports the

customer’s payment decision. The PR is added to the

customer PC or a mobile device. By modeling the cus-

tomer’s preferences using weighted fuzzy set mem-

berships, the fuzzy payment recommender agent

considers the preferences over the card issuers’ pol-

icies, such as credit limits, interest rates and many

others, as well as the policies imposed by the sec-

ondary issuers, such as employers, to suggest the

best combination of cards to the customer.

We present a prototype implementation of a pay-

ment recommender system, which allows split credit

card payments and helps the customer optimize the

distribution of his charges over multiple cards. The

payment recommender system can also be installed

on a client’s mobile device which can recommend

payment over one or more credit cards, which can be

particularly suitable for m-payments. For example, a

customer shopping in a store should able to use a

PDA to compute an optimal card split and then

transfer the solution by infrared or Bluetooth signal

to the cash register and finalize the transaction.

Contributions of this paper include (1) a flexible

payment recommender model to support the credit

card choices for a purchase that is based on fuzzy

membership of various card characteristics and pol-

icies, and user preferences; (2) It supports payment

with multiple credit cards for a purchase, which is

novel in the literature. Typically the current status

is to support micropayments, but our model could

support purchases with large payment amounts with

multiple cards, and support users with limited

credit; (3) The payment infrastructure with multiple

card transactions has been designed with minimal

changes to the existing payment infrastructure; (4)

A prototype system has been implemented to illus-

trate the feasibility of the proposed model; and (5) A

user survey was conducted to evaluate the user

acceptability of the flexible recommender system.

The rest of the paper consists of the following sec-

tions. The background review of the related litera-

ture is provided in Section 2. Section 3 describes our

divisible payment decision model based on fuzzy set

theory. Section 4 proposes the new divisible card pay-

ment infrastructure while comparing it with the

existing infrastructure, followed by the prototype of

the Payment Recommender system in Section 5. In

Section 6, we present and discuss the results of a

user evaluation of the flexible payment concept, and

Section 7 concludes the paper.

2. Related Work

Mobile payments (m-Payments) are defined as

payments that a mobile device carries out for e-com-

merce and/or standard commerce. M-commerce allows

such monetary transactions over a wireless network

and is estimated to reach $88 billion of the total glo-

bal m-commerce revenue by 2009 [12]. The number

of m-commerce transactions is expected to increase

as mobile devices with Internet access (such as cell

phones) become more prevalent. Herzberg [7] pro-

vides an overview of challenges and opportunities

involved in using such devices for making secure

payments and other banking transactions. Two chal-

lenges and opportunities of a mobile payment system

are providing secure transactions and convenience. It

requires proper mechanisms for authentication and

authorization via modular design that can ensure a

minimal number of fraudulent transactions. In addi-

tion, the mobile payments provide the convenience

that stems from the use of mobile devices to initiate

transactions no matter where they are (i.e. ubiquity),

and from the uniform interface to accommodate mul-

tiple transactions.

Although m-payment systems have recently

gained a lot of attention [14], most payment protocols

proposed for wireless networks focus on the perfor-

mance and security issues [15-17, 21, 25]. Lee et al.

[17] enumerated the requirements and properties of

mobile commerce payment systems as confidential-

ity, authentication, integrity, authorization, avail-

ability, and non-repudiation. Kungpisdan et al. [16]

proposed a protocol for secure mobile payment which

used symmetric-key encryption. The symmetric tech-

nique was applied to reduce the computation of the



parties involved and to satisfy the transaction secu-

rity properties. The proposed protocol offered the

ability to deal with failures and disputes among par-

ties. Kungpisdan et al. [15] also proposed a frame-

work for a practical mobile SET payment. Hwang et

al. [8] analyzed a previously-proposed m-payment

system and demonstrated its vulnerability. Cimato

[5] proposed an authentication protocol for GSM Jav-

acards. Popescu and Kanpskog [21] proposed an

anonymous mobile payment system that prevents

blackmailing based on the secure group blind sig-

nature scheme. Their biometrics-based system en-

abled the user of mobile devices to use fingerprint,

voice, or iris authentication, and other biometrics

technologies for secure online payments.

The existing mobile payment systems support

either prepaid, debit, or credit cards. As the name

implies, the prepaid method uses a SIM card that

stores electronic money or a virtual bank account

that can be accessed through message exchange sys-

tems such as SMS. The debit payment option is

made through ATM cards issued by the customer’s

own bank. It securely moves checking account funds

without the hassles or risks involved with paper

checks and without finance charges associated with

credit cards. Security is provided mostly with per-

sonal identification numbers (PIN). It is widely used

in Europe in retail businesses and it is predicted to

be even more widely used in the future.

Credit card payments are the most prevalent

method in e-commerce at present. The credit card

payment process is known to work almost anywhere

and people trust their trade labels like Visa or Mas-

terCard. Credit accounts are easy to implement

because they are less time critical: the details of pay-

ments made during daytime can be reported to the

credit card company as a batch job during the night

or according to the agreement between the merchant

and the credit card company. It is plausible to

assume that the people who use credit cards today

will use them as a payment method in m-commerce.

It is widely assumed that mobile devices will be inte-

grated with credit card payment systems [27], and in

this paper, we focus on extending the functionality of

the existing credit-card payment mechanism.

Much research on credit card payments for e-com-

merce focuses on security issues, such as fraud pre-

vention and reduction [24]. In order to reduce fraud,

the card issuing banks, such as American Express,

Discover, and MBNA, may issue a one-time tempo-

rary credit-card number (instead of a permanent

card number). A single-use credit card system was

studied by [22].

There have been studies of divisible e-cash pay-

ment protocols [3, 19]. These studies focus on pay-

ment solutions that ensure anonymity while allow-

ing electronic coins to be divisible. That is, each coin

can be spent incrementally as long as the total pur-

chases are limited to the monetary value of the coin.

These approaches deal with multiple purchases and

multiple merchants, while this paper is about one

purchase with one merchant but with multiple credit

card issuers.

Few studies on credit card payments focus on the

user-side issues. The customer may face a complex

utility optimization problem during each purchase.

The customer may need to determine which card or

which set of cards would be the best to use among

multiple cards for this particular purchase. The

user’s decision problem is to select a credit card or

a combination of cards to maximize her benefits

based on card features, such as incentives, interest

rates, etc. The solution of this decision problem will

be a recommendation of the best combination of

credit cards, so it can be viewed as a special kind of

a recommender system. While most of the early rec-

ommender systems are based on simple database

queries, our system is based on fuzzy logic [23].

Fuzzy logic has been used in many e-commerce

applications. It was used to handle the uncertainty

associated with customer choice when modeling

market structure [9]. The potential of a visitor as a

purchaser of services was classified using fuzzy

methods based on her demographic information [28].

Fuzzy logic was used to cluster customers into

several groups, based on their data [29]. Miao et al.

[18] conducted a case study on a recommender system

supporting automobile purchases. While these studies

discussed general and widely accepted practices of

fuzzy technology for e-commerce, we have used fuzzy



logic to model the customer’s perspective of credit-

card usage and solve the payment decision problem

based on the customer’s preferences.

Fuzzy technologies have also been applied to solve

optimization problems such as electronic production

assembly [11] and job grouping [9]. Similar to these

studies, we have applied the fuzzy technique to our

optimization problem. We believe that the card-

selection criteria, modeled by fuzzy sets, follow the

user’s intuitive requirements well. To model multiple

criteria, we have adapted the aspects of multi-objec-

tive optimization [6, 13]. Each criterion is represent-

ed by a fuzzy set and aggregated into an objective

function [11]. The user preferences for criteria are

flexibly weighted, such that the preferred criteria

will more significantly affect the output of an objec-

tive function [9]. Then, all the weighted criteria are

summed up to compute a single value and the best

solution is recommended based on that value [6, 13].

We have adapted this traditional approach to deal

with a variety of card features.

A preliminary survey of the user acceptance of

mobile payment decision support systems indicated

that card management is strongly favored, in addi-

tion to cost, security and convenience factors [25].

This paper proposes a “flexible payment recom-

mender system.” The proposed system recommends

the best combination of credit cards to use that is

consistent with the customer’s preferences, while

simplifying the potentially complex process of choos-

ing the card combination when many card features

and policies are to be considered.

A customer’s preferences and spending habits will

affect the use of a particular card. This has led us to

consider the personalization and customization

issues. Acquiring user preferences by asking them,

however, should be done carefully because of the phe-

nomenon, called “paradox of the active user” [2]. It

implies that a user is irrational in that she does not

want to spend initial time to set up the system envi-

ronment although it will bring some benefits in the

long term. Therefore, a system should be equipped

with reasonable default values for its parameters

and do its computations with a minimum of user

inputs, if possible. In our system, the initial default

values are obtained through statistical analysis of an

off-line user survey described in Section 6.

Apart from a user’s self-profiling, the prevailing

methods used for personalization are based on time-

series historical records (e.g., the items purchased

previously by the customer are used in Amazon.

com’s personalization) and/or horizontal survey

records (i.e., purchasing patterns of millions of buy-

ers adapted in a marketing strategy). Both methods

refrain from requiring the user to do extra work for

setting up her preferences, but implement a system

to learn users’ preferences and/or users’ behavioral

patterns. In our system, the survey method has been

used for estimating a typical user’s preferences, and

fuzzy sets have been formulated for user personal-

ization.

3. The Divisible Card Payment Decision

Model

The credit card(s) selection involves decision mak-

ing with multi-criteria, and user’s evaluation or pref-

erence on one criterion over another criterion may

not be always precise. In addition, each person may

exhibit different decision behaviors in different con-

texts. The same person may evaluate the same set

of criteria in evaluating the credit cards based on the

situation or context one is in. For example, one will

make a different decision on the priority of credit

card criteria when he/she is under tight financial

constraint and when one is under normal situation

with no additional stress, etc. For instance, one

would rate the interest rate more important than the

promotional incentives in the first case, while in the

second situation, one may be more interested in the

promotional incentives. This situational variation

needs to be captured in the decision model. Thus, the

traditional decision models based on precise values

or weights in one normal circumstance may not cap-

ture the real complex decision making situations. In

other words, one needs to decide the situation assess-

ment of oneself, and then prioritize the evaluative

criteria accordingly. In order to capture this decision

making behavior in different situations, we use a

pair-wise comparison of the situations, and find dif-



ferent weights for each situation. This is similar to

a group decision making even though only one per-

son is involved in evaluating the criteria in judging

the cards. It is as if there is different persona of one

person and each persona influences differently in

decision making. The priority vector (weights) of dif-

ferent persona will be computed in our model.

Often users use evaluative or subjective linguistic

expressions to express the card preferences or the

criteria preferences. For instance, Amex card A is

rated “very good” while Master card M is “good”, or

the interest rates can be rated “very important” in a

situation A, while the promotional incentive factor is

“low in importance” in the same situation, and so on.

To reflect this imprecise nature of user’s preference

criteria in credit card rankings and payment divi-

sions, we model the credit card payment decision

problem with a fuzzy set theory combined with the

Analytical Hierarchy Process (AHP) method.

To solve the divisible payment decision problem,

we adopt fuzzy set theory and user-chosen weights

and thresholds, as described below.

Given a set of credit cards C={c1, c2, cn}, and a

set of policies or criteria applicable to each card,

P={p1, p2, pm}, we define the customer’s preferences

for different policies as weights dependent on the pol-

icies. More specifically, we define the customer’s

preference for policy j on card i as wi(pj), where

, , and .

At present, we assume the customer does not

distinguish between the same policies on different

cards. That is, the same weights are used for the

same policies on all the n credit cards (i.e.,

=…= ). We use w(pj) instead of

wi(pj). For instance, if a customer assigns a higher

weight to the interest rate and a lower weight to the

frequent flyer miles, she is assumed to have the same

weights for the interest rate and frequent flyer miles

policies on all of her cards. Of course, the preferences

of different policies and criteria vary among custo-

mers.

The customer may have a preferred value for each

policy. For example, an interest rate below the prime

rate or 1,000 frequent flyer miles might be viewed as

favorable. This is modeled by a threshold value of

policy j, t(pj). Similar to the case with weights, we use

the same threshold value for the same policy on

different cards. Each policy j on each credit card i is

viewed to have a value, vi(pj). When this value is

below the threshold value, the degrees of goodness of

the policy can be measured. The degree of goodness

of a policy is computed through fuzzy membership

values. Equation (1) represents the fuzzy value for

policy j on card i, fi(pj). The fuzzy membership value

is “1” if the corresponding value of the policy/criterion

is equal to or greater than the threshold value.

Otherwise, the membership value will be calculated

by the first part of equation (1).

fj(pj)= (1)

The fuzzy membership values are determined for

0 wi pj( ) 1≤ ≤ 1 i n≤ ≤ 1 j m≤ ≤

w1 pj( )=w2 pj( ) wn pj( )

vi pj( )t pj( )------------- , if vi pj( )<t pj( )

1 , otherwise⎩⎪⎨⎪⎧

Figure 1. Examples of fuzzy membership functions



each criterion or policy of each credit card separately.

There are two types of criteria. First, the larger the

value of the criterion is, the more desired it is by the

user, as shown in Figure 1(a). Second, the smaller

the value of the criterion is, the more desired it is,

as shown in Figure 2(b). To reduce the complexity of

the user interface and to enhance user friendliness

for the population that is not familiar with fuzzy sets,

we use the membership functions1 as shown in

Figure 3. Figure 1(a) and Figure 1(b) show the

shapes of a right and a left shoulder, respectively.

More complex user-defined fuzzy functions can be

used at the expense of additional computing over-

head. Note that the fuzzy membership function is not

generated until the user determines the threshold

value.

Figure 2 shows a fuzzy membership function for a

policy/criterion of the closing date. Note that the

criterion used is the number of days remaining until

the closing date (i.e., the difference between the

closing date and the current date). Most people

would prefer to use a credit card with the closing

date further away. In Figure 2, “10” is used as the

threshold value, and the fuzzy membership values

for the remaining days of 4, 6, 8, 13, and 18 are 0.4,

0.6, 0.8, 1, and 1, respectively. For some credit card

usage policies, such as “the credit card can only be

used to purchase office supplies, the criterion is

binary, and the fuzzy membership function gener-

ates either 1 or 0 as shown in Figure 3.

Using the membership function and the preference

weights for each policy, the PR agent computes the

optimal card combination through the following

steps. First, the weights and fuzzy sets for credit card

policies and criteria are initialized based on the

customer’s preferences. The criteria modeled in this

paper include the amount of available credit before

reaching the limit, the interest rate, the closing date,

and the bonus rate. Second, the fuzzy membership

value of each criterion of each card is determined by

the corresponding fuzzy set. Third, the PR adds up

the weighted fuzzy membership values of the criteria

of each card and generates a payment suggestion

based on the card with the maximum value. Finally,

the PR proposes to the user to pay the purchase

amount with the selected credit card. If the selected

credit card does not have enough available credit for

the whole payment, the PR repeatedly performs thehttp:// www.research.ibm.com/able/doc/reference/com/ibm/

able/rules/doc-files/arlDefSets.html

Figure 2. A fuzzy membership function for the closing date

Figure 3. A fuzzy membership function for a binary

criterion



above procedure until a combination of credit cards

covers the total purchase price.

The payment recommender algorithm is shown in

Algorithm 1. Let n and m be the number of credit

cards and the number of policies, respectively. Let ci

be credit card i. Let fi(pj) and w(pj) be the fuzzy set

membership value and the weight of policy j on card

i, respectively. Let Evali be the evaluation value

summing up all the weighted fuzzy membership

values of card i. Let CT be the card selected for

payment at the T-th iteration and Totalamount be

the total purchase amount. Let Availi and Payi be the

available credit and the payment of card i, respec-

tively.

Algorithm 1: Fuzzy Payment Recommender Algo-

rithm

1. Define the customer’s preferences by initializing

the weight, w(pj), and the threshold value, t(pj),

for each criterion/policy.

2. Calculate the fuzzy membership value, fi(pj), for

each criterion/policy on each credit card, where

, and .

3. Obtain the evaluation value, Evali for each credit

card ci by adding up the fuzzy membership values.

(2)

4. Determine the best card among those with

remaining credit limit by finding the one with the

highest evaluation value.

CT = (Evali), where AvailT > 0 (3)

5. If , pay the whole amount

with the best card T and adjust the following:

AvailT= AvailT − Totalamount, PayT= Totalamount,

and Totalamount=0.

Otherwise, make a partial payment with the best

card selected and adjust the following:

PayT= AvailT, AvailT= 0, and Totalamount =

Totalamount −AvailT

6. If Totalamount = 0, recommend the list of cards,

{Ci}, with the suggested payment amount for each

card. Stop.

Otherwise, goto step 3.

4. The Flexible Card Payment Processing

Infrastructure

In order to handle the proposed payment with

multiple cards the existing payment processing

infrastructure needs to be extended [20]. The current

infrastructure for the credit card payment processing

is depicted in Figure 4 and described as follows.2 The

customer finds the desired products from the mer-

chant’s Web site (Step 1); When she is ready to make

a payment, the customer enters the billing informa-

tion on the merchant’s secure payment gateway

(Step 2); The payment gateway provides the billing

information (such as credit card number, card-

holder’s name, billing address, expiration date, etc.)

to the merchant account that has been set up by the

online merchant account provider (Step 3). The mer-

chant account provider offers the credit card pro-

cessing service for each merchant account. It trans-

fers the billing information to the issuing bank of the

customer’s credit card for authorization (Step 4). The

issuing bank checks if the credit card information is

valid and if the credit card has a sufficient balance

to cover the purchase. If so, it sets aside the amount

of purchase from the customer’s account for the mer-

chant. If the credit card is invalid or the credit limit

has been reached, the issuing bank sends back a

denial code to the merchant account (Step 5). The

approval (or denial) code is passed to the payment

gateway back from the secure merchant site (Step 6).

The approval code is passed to the customer. Usu-

ally, the payment gateway emails the customer the

payment receipt (Step 7). At the end of the day, the

merchant requests to settle all the transactions of

the day. The merchant account provider offers the

merchant account settling service. It sends the

request to capture funds to the acquiring bank (Step

8). The acquiring bank forwards the request to the

issuing bank (Step 9). The card issuing bank pays

1 i n≤ ≤ 1 j m≤ ≤

Evali=

j 1=

m

∑ fi pj( ) w pj( )×

Maxi 1=

n

AvailT Totalamount>

See http://www.electronictransfer.com/ for more details.



funds to the acquiring bank and the funds are depos-

ited to the merchant’s bank account. The actual

funds reach the merchant’s checking account in

approximately two business days (Step 10).

To support flexible card payments, the existing

infrastructure needs two modifications. First, a soft-

ware agent called Payment Recommender (PR) is

added to the client side. For each purchase, the PR

recommends to the customer the optimal combina-

tion of credit cards to use. If the customer accepts its

suggestion, the PR generates the Virtual card (V-

card in short) which is a set of recommended cards

and their respective payment amounts. The PR gen-

erates a unique V-card number, the total amount in

the card, the timestamp, and the billing information

of all the recommended cards. As the effectiveness of

the PR affects the performance of the whole payment

system, developing an effective and efficient PR that

can accommodate numerous card features and pol-

icies is an important research issue. We discuss a

prototype payment recommender, called fuzzy pay-

ment recommender (fuzzy PR), in Section 5.

Second, special software called a Virtual Card

Manager (VCM) is added at the merchant side to

handle the V-card payment request. When the V-

card is up for approval, the VCM decrypts the divis-

ible payment information (i.e., which cards and what

amounts should be charged for the V-card purchase),

and forwards the billing information to each card

issuer involved in the V-card. Unlike the current pro-

tocol that contacts one credit card issuer for ap-

proval, the VCM needs to communicate with all the

issuing banks mentioned in the V-card. The VCM

could process each card approval sequentially, one at

a time, or request the approval of multiple cards con-

currently. Each card-issuing bank sends back the

approval code to the VCM. When all the approval

codes have been received, VCM sends back the ap-

proval of the V-card to the payment gateway.

Figure 5 depicts the proposed infrastructure for

the flexible card payment processing infrastructure.

First, the customer finds the desired products from

the merchant’s Web site. The Payment Recommender

agent computes the optimal card combination for the

payment and generates a V-card (Step 1). The V-card

information is passed to the payment gateway and

the V-card billing information is transferred to the

VCM of the merchant’s account. (Steps 2-3). The

VCM transfers the billing information for approval to

the issuing banks of each credit card used in the V-

card (Step 4). Each issuing bank checks whether the

credit card information is valid and whether the

credit card has sufficient funds. If so, it sets aside the

amount of purchase for the merchant. Each issuing

bank in the V-card sends back the approval (or

denial) code to the merchant’s VCM (Step 5). The

VCM waits until all pertinent card issuers send back

their approval (or denial) codes. When the approval

codes from all card issuers in the V-card are avail-

able, the VCM generates an approval code for the V-

card, and forwards the code to the payment gateway

(Step 6). The approval code is passed to the customer.

Figure 4. The Existing Credit Card Payment Infrastructure



The payment gateway emails the customer a pay-

ment receipt. The PR adjusts the credit card bal-

ances resulting from the current purchase with the

V-card (Step 7). At the end of the day, the merchant

requests to settle all the transactions of the day. Step

8 to 10 are the same as in the existing processing

infrastructure, namely, the merchant account sends

the request to capture funds to the acquiring bank.

The acquiring bank forwards the request to the

issuing banks. The card issuing banks pay funds to

the acquiring bank and the funds are deposited to

the merchant’s bank account. The actual funds reach

the merchant’s checking account in approximately

two business days.

At Step 5, if any one of the issuing banks denies

the billing request, the V-card transaction is consid-

ered denied, and any approved requests should be

nullified. That is, the V-card approval request is han-

dled as an atomic transaction with multiple approval

requests. The V-card approval request, as an atomic

transaction, consists of distributed approval requests

(Tygar 1998) to each card issuer, similar to those in

a transaction processing system. For example, a V-

card transaction may consist of three approval re-

quests to the card issuers A, B and C, respectively.

As in a transactional system, either all of the re-

quests in the V-card need to be approved, or none of

them is considered approved. Suppose, A and B have

sent back approval codes, but C sent back a denial

code. Then the whole transaction (i.e. the approval

request to use the V-card) is considered denied, and

all other approvals are rolled back. This atomic prop-

erty of the V-card approval transaction ensures no

partial approvals or partial denials are possible. The

VCM takes care of this atomicity of the V-card’s

approval process.

As shown above, the flexible card payment archi-

tecture requires minimal modifications to the exist-

ing infrastructure, namely the PR (Step 1 above) and

VCM (Steps 4-6 above), while allowing flexible pay-

ment using multiple cards for a single purchase.

5. Payment Recommender Prototype System

We have developed the Web-based prototype of the

Fuzzy Payment Recommender system using JSP

and deployed it on an Apache Web server. The

prototype system provides two major functionalities

of PR: managing multiple cards and generating a

recommendation of cards for a purchase. The proto-

type also simulates the V-Card Manager that performs

the multiple credit card approval process.

5.1 Card management Component

This component allows the customer to register

and log in. When the customer logs into the PR

system, the main menu comes up with three sub-

menus: ‘My Card List’, ‘My Preference’, and ‘Create

A V-Card’. The ‘My Card List’ allows the customer

to view the list of all her cards, add an additional

card, to specify card features, such as interest rate,

Figure 5. The Flexible Card Payment Infrastructure



balance, etc. It is intended to connect to the card

provider’s server to automatically receive these

properties, such as the existing balance information

(and therefore the currently available credit). This is

similar to the feature of automatically receiving the

existing transactions as used in commercial personal

finance software, such as Quicken and Money. In the

prototype, preferences and features of cards are

captured through a series of simple questions.

5.2 Payment Recommendation Component

This component generates a virtual card, i.e. one

or more cards to be used for the payment. When the

customer wants to purchase a product, she enters the

purchase amount and pushes the “Recommend cards”

button. The PR performs the fuzzy membership-

based optimization described in Section 3. For ex-

ample, the customer enters the purchase amount of

$500. This information, in conjunction with the pre-

viously entered information about credit card fea-

tures and usage preferences, is used to perform the

optimization. Figure 6 shows that the PR considers

the card features such as closing date, interest rate

and bonus rate, and shows the fuzzy membership

value of each feature for each card. The preference

weights based on the user’s card usage preferences

are also used.

The PR system generates a list of cards to be used

and the amount of charge against each card, creates

a virtual card (V-Card), and logs its information,

such as transaction number and amount, timestamp,

expiration date and current status.

5.3 Virtual Card Processing Component

With the user’s confirmation, the V-Card infor-

mation is sent to the merchant bank where the

Virtual Card Manager (VCM) handles the V-card

approval process with each card’s issuing bank. We

have implemented a VCM component with simula-

tions of three credit card issuing banks. For simpli-

city, each credit card bank has a back-end database

with balance information. When the simulated card

issuing bank receives the approval request from the

VCM, its server authenticates the account infor-

mation and checks the available credit line in the

database to see whether this transaction amount is

valid. After validation, each issuing bank returns

either a denial message or an approval code back to

Figure 6. The Fuzzy Payment Optimization



the VCM. The approval/denial code is then sent back

to the PR system, which adjusts the balance amount

of each card involved in the particular transaction.

After a V-card purchase has been approved, the

transaction information is recorded into the database

of the merchant site. At the end of the day, the

merchant can log into the acquiring bank and re-

quests to settle all the transactions of the day. The

merchant account sends the requests to each involv-

ed card issuing bank to capture funds, and the funds

are deposited into each acquiring bank. The issuing

banks update their own databases by reducing the

available credit. For the acquiring banks, the update

increases the current balance.

5.4 Implementation Options

The Payment Recommender system can be imple-

mented on Electronic commerce sites or on a smart

phone. Recent mobile payment infrastructure by

Google, called “Google Wallet”, allows credit card

payments using smart phones and Near Field Com-

munication wireless network [30]. However, Google

Wallet is not allowing multiple card payments for a

single purchase. Our PR system can be implemented

as a mobile app for smart phones, and use the exist-

ing payment infrastructure, like Google Wallet,

using one credit card as the only option. Or it can

extend the mobile payment infrastructure to connect

to the several credit card companies for a payment,

when multiple card payment option is turned on. The

option of having the end users to configure the loy-

alty and discount benefits, not just from Google pro-

viders, and automatically recommending credit cards

(one or more) can add values for the customers’ pur-

chase experience.

6. Survey Studies on Payment Recommen-

der System

To find out the general public’s perception and

acceptance of the PR system and to find out the

major factors in card payment decisions, we con-

ducted a preliminary survey on 68 subjects with 24

females, 43 males and 1 unspecified. The survey

questionnaire was posted on the Web and the sta-

tistical data analysis was performed using the SAS

package.

6.1 General Acceptability Analysis

First, we investigated the correlations among var-

ious variables to see whether there exist linear rela-

tionships between them. In the correlation matrix

(see Appendix A), the Pearson correlation coefficients

indicate the strength of the linear relationship bet-

ween two variables. The matrix shows that the cor-

relation between “age (AE)” and “number of cards a

user has (NC)” is 0.58 at the significance level 0.01.

There is a positive linear relationship between “age

(AE)” and “income (IE)” at the significance level of

0.01. The variable, “years a user has used a computer

(YC)” has a positive relationship with “years a user

has used the Web (YW)” and “the number of cards

a user owns (NC)” at the significance level of 0.01.

We conducted the correlation analysis on three

variables: “willingness to use a mechanism which

allows several cards for single online purchase (WM)”,

“willingness to use computer software to manage

credit cards (WP)” and “willingness to use a personal

card payment recommender software with automatic

filling of payment information (WA)”. The three vari-

ables, WM, WP and WA, are considered to be the

indicators of the usability and the user acceptance of

the flexible payment system.

The value of the correlation coefficient between

WM and WP is 0.49 and between WP and WA is

0.58, both of which indicate positive relationships at

the significance level of 0.01. The correlation coeffi-

cient between WM and WA is 0.30, which indicates

a weak relationship between the two at the signif-

icance level of 0.05.

Only the “feeling of comfort with credit card(s)

(CC)” has minor relationships with WP and WA with

the coefficient values of 0.29 and 0.36, respectively,

at the significant level of 0.05. We could not find any

significant relationships between other variables

(e.g., age, income, etc.) and the three variables WM,

WP and WA. We concluded that a comfortable feel-

ing with a credit card(s) can potentially be a factor



that affects the usability and user acceptance of the

flexible payment system.

Secondly, we investigated the medians of WM, WP

and WA on a 5 Likert-scale as shown in Table 1. In

the 5 Likert-scale used in the survey, 4 meant “agree”

and 3 meant “neutral.” For example, for the question

“I would gladly use computer software to manage my

credit cards (i.e., Payment Recommender System

which recommends the best combination of available

cards for your online purchases),” subjects answered

“agree” on average. We note that each of the three

usability indicator variables forms a normal distri-

bution according to Kolmogorov-Smirnov analysis.

Subjects showed positive answers with mean 3.44 in

using a divisible card payment system (i.e.,WM) and

in using computer software to mange credit cards

(i.e., WP) with mean 3.41. Compared with WM and

WP, on the other hand, subjects showed less interest

in auto filling of payment information (WA) with

mean 3.05.

Finally, we investigated potential concerns about

the flexible payment system. Two open-ended ques-

tions were given: (a) What feature is most important

for you in a personal card management system? and

(b) What would stop you from using a personal card

management system? Figure 7(a) summarizes the

answers to the question (a), and Figure 7(b) sum-

marizes the answers to the question (b). As shown in

Figure 7, the public is most concerned with security.

Other features such as convenience, alerts, consoli-

dation, privacy, incentives like discounts, speed, and

user friendliness are also found to be desirable for a

good payment recommender system.

6.2 Factor analysis to identify the underlying

major factors

We applied factor analysis to identify major factors

and to evaluate the relative importance among the

identified factors. First, various criteria were re-

duced to several main factors which explain the vari-

ances in the data points of the user answers. Second,

the final commonality estimates derived from factor

analysis were applied to evaluate the relative impor-

tance among the factors. Finally, the importance of

each criterion for its corresponding factor was ana-

lyzed. This information is used as the weight in the

weighted-sum approach of multi-criteria optimiza-

tion in an example.

(1) Factor analysis for classifying card selec-

tion criteria: Given the question in our survey,

“The following are important when choosing one

credit card over another. What do you think?” with

the specification of 12 criteria used in choosing one

card over another (e.g., frequent flyer miles, etc.),

subjects marked their answers on the 5 Likert scale.

To figure out the underlying important factors (prin-

ciples) over multiple criteria, we used factor analysis.

Our assumption was that a variety of criteria could

be reduced to a few important factors which effec-

tively explain the variances of user answers on cri-

teria and rank each criterion within its correspond-

Figure 7. Users’ concerns about a payment recommender system



ing factor.

Table 1 shows rotated factor loadings. We obtained

3 factors from 12 criteria.

The numbers in Table 1 represent the correlations

between three factors and the criteria after rotation.

Shaded boxes show strong correlations of each factor

corresponding criterion. The correlation between

each criterion and its corresponding factor contrib-

utes to computing the weight of a criterion. “Cash

Back” is most correlated with factor 1 and “Discount

Rate” is least correlated with factor 1. “Interest

Rates” is most correlated with factor 2 followed by

“Annual Fee” and “Available Balance”. “Closing

Date” is most correlated with factor 3 followed by

“Card Brand.”

(2) The importance of each factor in the total

commonality estimates: By observing the strength

of correlations between the factors and the corre-

sponding features, we realized that the first factor

deals with policies/criteria that provide potential

incentives to users. We named this factor Incentives.

The second factor deals with numeric features of a

card, so we named it Costs. The third factor is named

Perception, which deals with nonnumeric features

such as a card brand or reputation. The variances

explained by factor 1, 2 and 3 were 3.692, 1.529 and

0.93, respectively. The total commonality estimate

(i.e., the total estimate of the proportion of common

variance in a variable) was 6.153. Figure 8 shows the

break down of the variance. The first factor, Incen-

tives, accounts for most of the variance, followed by

Costs and Perception.

6.3 An example of evaluating a credit card using

the identified factors

Based on the survey analysis, credit card users

seem to prefer a card that provides a feature(s) called

Incentives. Among the policies/criteria belonging to

this group, “Cash Back” is the most important to

credit card users. Therefore, “Cash Back” which

belongs to factor 1 has a higher weight than any

other feature. On the other hand, “Interest Rate”

which belongs to Costs is another considerably

important feature.

The formula shown in the step 3 of the Fuzzy Pay-

ment Recommender Algorithm 1 is illustrated as fol-

lows:

Evali=

, where l is the number of factors.

The range of i is , and the range of j is

, where n is the number of credit cards a

user has and m is the number of credit card features/

criteria classified with each factor. In the survey

above, m = where m1=7, m2=3, and m3=2, as

shown in Table 1; ck is the proportion of the total

commonality estimates explained by the k-th factor,

which was computed in Figure 8; is the fuzzy

membership value of the j-th criterion which belongs

to the k-th factor; is the importance (weight)

of the j-th criterion which belongs to the k-th factor.

Note that the weight is the correlation between the

k 1=

l

∑ ck

j 1=

mk

∑ fi pjk( )*w pj

k( )⎝ ⎠⎜ ⎟⎛ ⎞

×⎝ ⎠⎜ ⎟⎛ ⎞

=

k 1=

l

∑

j 1=

mk

∑ ck* pj

k( )

*w pjk( )

1 i n≤ ≤

1 j m≤ ≤

k 1=

l

∑

fi pjk( )

w pjk( )

Table 1. Rotated Factor Loadings

Factor: 1

(Incentives)

Factor: 2

(Costs)

Factor: 3

(Perception)

Cash Back 0.78628 0.08356 -0.04894

Shopping Benefits 0.72902 -0.04621 0.25708

Fraud Insurance 0.71901 0.04097 0.10903

Award Package 0.70659 0.20399 0.08128

Purchase Protection 0.68083 0.04002 0.06552

Frequent Flyer Miles 0.66427 0.04594 0.23345

Discount Rate 0.63552 0.28317 -0.00643

Interest Rates -0.07788 0.89948 0.19549

Annual Fee 0.03169 0.63134 -0.09360

Available Balance 0.12489 0.32993 0.25644

Closing Date 0.08016 0.10106 0.69207

Card Brand (e.g. VISA) 0.11255 0.00253 0.66072

Figure 8. Variances explained by three factors



j-th criterion and the k-th factor, shown in shaded

areas in Table 1.

An example of evaluating credit card i with three

factors (i.e., l = 3) is as follows.

Evali =

Let’s focus on the third term, .

where = Card Brand (e.g. VISA), =Closing

Date, =0.66072, =0.69207 from Table 1

and c3=0.15, the perception factor, from Figure 8.

Assuming the fuzzy membership values of

=0.5 and =0.7, we can compute the third term

as follows.

.

That is, the value of the third term of the evaluation

function, Evali, is 0.12. Once the first and second

terms are computed, a value for the evaluation of the

i-th card is obtained. After that, computation with

the formula in step 4 of Algorithm 1 is performed to

eventually find the best combination of credit cards.

7. Conclusions and Future Work

This paper presents the flexible card payment

infrastructure for mobile commerce that recom-

mends one or more credit cards for acustomer. The

recommender system can suggest multiple cards to

divide the payment of a single purchase. The pro-

posed payment system uses the client Payment Rec-

ommender (PR) agent and the merchant’s card

approval management software called Virtual Card

Manager (VCM). The major benefits of our proposed

system include the following. First, the proposed

solution requires minimal modifications to the exist-

ing online and mobile payment infrastructure. Sec-

ond, the fuzzy set based payment recommender (PR)

agent adds value to the credit-card users by custom-

izing each payment according to the user’s prefer-

ences. The use of weighted criteria in selecting a card

over another allows users to express relative pref-

erences between criteria. The use of fuzzy sets allows

time-dependent and parameter-based computations

which determine the evaluation of a card on a daily

basis.

We provided a detailed Payment Recommender

algorithm and developed a prototype system to dem-

onstrate the functionalities of the proposed PR agent.

We also conducted a user-acceptability survey of the

proposed flexible payment approach and of the user

preference criteria in card selection. The factor anal-

ysis of different criteria was performed to compute

the relative importance of the each card selection cri-

terion and an example was provided to illustrate how

the differences in a user’s preferences are reflected

in the algorithm in terms of different weights.

Future research issues include extending the rec-

ommender system to utilize the user’s transaction

and payment histories to derive the credit card

related preferences, identifying security challenges

in mobile payment with the new infrastructure, and

finding a feasible business model to minimize the

costs involved in the merchants server side trans-

actions generated by using multiple cards.

References

[1] Barnes, S. “The Mobile Commerce Value Chain: Anal-

ysis and Future Developments”, International Journal

of Information Managemet, Vol. 22, pp. 91-108, 2002.

[2] Carroll, J.M. and M.B. Rosson, “The Paradox of the

Active User”. In J.M. Carroll (Ed.), Interfacing Thought:

Cognitive Aspects of Human-Computer Interaction.

Cambridge, MA: MIT Press, 1987.

[3] Chan, A.,Y. Frankel and Y. Tsiounis, “Easy Come

Easy Go Divisible Cash”, Proceedings of Eurocrypt '98

(Lecture Notes in Computer Science). Springer-Verlag,

1998. Available at http://www.ccs.neu.edu/home/yian-

nis/pubs.html.

k 1=

l

∑

j 1=

mk

∑ ck*fi pj

k( )*w pjk( )=

j 1=

m1

∑ c1*fi pj

1( )*w pj1( )

+

j 1=

m2

∑ c2*fi pj

2( )*w pj2( )=

j 1=

m3

∑ c3*fi pj

3( )*w pj3( )

j 1=

m3

∑ c3*fi pj

3( )*w pj3( )

p1

3p2

3

w p1

3( ) w p2

3( )

f1 p1

3( )

f2 p2

3( )

j 1=

m3

∑ c3*fi pj

3( )*w pj3( )=0.15×

j 1=

m3

∑ fi pj3( )*w pj

3( )⎝ ⎠⎜ ⎟⎛ ⎞

=0.15×

j 1=

2

∑ fi pj3( )*w pj

3( )⎝ ⎠⎜ ⎟⎛ ⎞

=0.15× f1 p1

3( )*w p1

3( )+f1 p2

3( )*w p2

3( )( )

=0.15*f1 p1

3( )*w p1

3( )+0.15*f1 p2

3( )*w p2

3( )

=0.15*0.66072*0.5+0.15*0.69207*0.7=0.12



[4] Chun, S., Y. An, J. Geller and S. Park, Fuzzy Virtual

Card Agent for Customizing Divisible Card Payments,

K. Bauknecht, B. Proll and H. Werthner (eds.) Lecture

Notes in Computer Science 3590, E-Commerce and

Web Technologies, the 6th International Conference

(EC-Web 05), Springer, 2005, pp 287-296.

[5] Cimato, S. “ Design of Authentication Protocol for

GSM Javacards”, LNCS Vol. 2288, 2002.

[6] Fuller, R and C. Carlsson, “Fuzzy Multiple Criteria

Decision Making: Recent Developments”, Fuzzy Sets

and Systems, Vol. 78, No. 2, pp. 139–153, 1996.

[7] Herzberg, A. “Payments and banking with mobile per-

sonal devices”. Communications of the ACM, Vol 46,

No 5, May 2003.

[8] Hwang, S. D. Lee, D. Han and J. Ryou. “Vulnerability

of a Mobile Payment System.” International Workshop

on Information Security Application, 2004.

[9] Jain, V. and R. Krishnapuram, “Applications of Fuzzy

Sets in Personalization for e-Commerce”, Proceedings

of IFSA-NAFIPS 2001 Conference, July 2001.

[10] Johtela, T, J. Smed, M. Johnsson, and O. Nevalainen,

“Fuzzy Approach for Modeling Multiple Criteria in

The Job Grouping Problem”, In M. I. Dessoyky, S. M.

Waly, and M. S. Eid, editors, Proceedings of the 25th

International Conference on Computers & Industrial

Engineering, pp. 447-450, New Orleans, LA, Mar,

1999.

[11] Johtela, T, J. Smed, M. Johnsson, and O. Nevalainen,

“Single Machine Scheduling with Fuzzy Multiple Cri-

teria Optimization in Electronic Assembly”, in Ylini-

emi and Juuso (eds.), Proceedings of TOOLMET'98

Symposium, pp. 121-126, Oulu, Finland, 1998.

[12] Jupiter Research Corporation, Mobile Commerce in

Europe, Marketresearch.com, August, 2004.

[13] Kim, I. Y. and de Weck, O, “Adaptive Weighted Sum

Method for Bi-objective Optimization”, Structural and

Multidisciplinary Optimization, Vol. 29, pp. 149-158,

2005.

[14] Krueger, M. “The future of M-payments-Business

Option and Policy Issues” Background Paper No.2.,

Electronic payment Systems Observatory (ePSO) August

2001. Available at http://epso.intrasoft.lu/papers/Back-

grnd-2.pdf

[15] Kungpisdan, S. B. Srinivasa, P. D. Le, “A Practical

Framework for Mobile SET Payment”, International e-

Society Conference, 2003.

[16] Kungpisdan, S. B. Srinivasa, P. D. Le. “A Secure

Account-based Mobile Payment Protocol”, IEEE Inter-

national Conference on Information Technology: Coding

and Computing. 2004.

[17] Lee, C. W. Kou, and W. Hu. “Mobile Commerce Secu-

rity and Payment Methods”, Advances in Security and

Payment Methods for Mobile Commerce. Idea Group,

Inc. 2004.

[18] Miao, C., Q. Yang, H. Fang, and A. Goh,. “Fuzzy Cog-

nitive Agents for Personal Recommendation”, Proceed-

ings of the 3rd International Conference on Web Infor-

mation Systems Engineering, 2002.

[19] Nakanishi, T. and Y. Sugiyama. “Unlinkable Divisible

Electronic Cash,” Proceedings of Third International

Information Security Workshop, ISW 2000, Australia,

2000, Lecture Notes in Computer Science 1975, pp.

121-134, Springer, 2000.

[20] Park, S., Chun, S. and Cho, J., “An Infrastructure for

Customizable and Divisible Card Payments for Online

Purchases,” Proceedings of the 35th Annual Meeting of

the Decision Sciences Institute (DSI 2004), Boston, MA,

2004, pp 5091-5096.

[21] Popescu, C. S. Knapskog. “An Anonymous Mobile

Payment System based on a Group Blind Signature

Scheme”. Proceedings of the IST Mobile & Wireless

Communications Summit 2004, June 2004.

[22] Rubin, A. D. and R. N. Wright, “Off-Line Generation

of Limited-Use Credit Card Numbers.Financial Cryp-

tography, pp. 196-209, 2001.

[23] Schafer, J. B., J. A. Konstan and J. Riedl, “E-commerce

Recommendation Applications”, Data Mining and

Knowledge Discovery. Vol.5, pp115-152, 2001.

[24] Shankar, U. and M. Walker, “A Survey of Security in

Online Credit Card Payments,” UC Berkeley Class

Notes, May, 2001.

[25] Tech Republic, “Enabling Secure, Interoperable, and

User-Friendly Mobile Payments”, Mobile Payment

Forum White Paper 2002. Available at http://whitepa-

pers.techrepublic.com.com/whitepa-

per.aspx?docid=69050

[26] Tygar, J. D. “Atomicity versus Anonymity: Distributed

Transactions for Electronic Commerce,” Proceedings of

the 24th VLDB Conference, New York, USA 1998.

[27] WirelessDevNet.com, “Mobile Payments: Death of

Cash and the Credit Card?”, Available at http://

www.wirelessdevnet.com/news/2004/sep/24/

news3.html

[28] Yager, R., “Fuzzy Methods in E-Commerce”, Annual

Conference of the North American Fuzzy Information

Processing Society - NAFIPS 1999, p 5-11.

[29] Zhang, Y., M. Shteynberg, S.K. Parasad and R. Sun-

derraman, “Granular Fuzzy Web Intelligence Tech-

niques for Profitable Data Mining”, Proceedings of

2003 IEEE International Conference on Fuzzy Sys-

tems, pp. 1462-1464, May, 2003.

[30] Google, “Google Wallet” [computer software], 2011.



Available at http://www.google.com/wallet/

Appendix A– Correlation Matrix

Correlation analysis is used to find the linear rela-

tionship between two variables. We designed 14 vari-

ables for correlation analysis and abbreviation for

the variables is as follows:

AE : age, GR: Gender, IE: Income

YC : Years a user has used a computer (i.e., ques-

tion 4)

YW : Years a user has used Web (i.e., question 5)

FP : Frequency a user purchase product or services

on the Web (i.e., question 6)

NC : Number of cards a user owns (i.e., question 7)

AB : Average balance a user carries on each one of

his credit card (i.e., question 8)

FC : Frequency a user use a credit card for online

purchase (i.e., question 9)

SP : Average size of one of online purchases (i.e.,

question 10)

CC : Comfort to use credit card(s) (i.e., question 12)

WM: Willingness to use a mechanism which allow

several cards for single online purchase (i.e.,

question 14)

WP : Willingness to use computer software to man-

age credit cards (i.e., question 15)

WA : Willingness to use a Personal Card Manager

with auto filling payment information (i.e.,

question 16)

The table below is correlation matrix with the list of

variables. The numbers in the table are “Person Cor-

relation Coefficients” which indicate the strength of

the linear relationships between two variables. The

range of the numbers is from −1 to +1 where −1 indi-

cates a perfect negative correlation and +1 indicates

a perfect positive correlation. For example, the 0.58

on the position of NC (i.e., number of cards a user

owns) and AE (i.e., age) in the matrix says that the

tightness with which data points of NC and AE are

around the line is 58 percent. High correlation means

that the two variables are tend to form a linear rela-

tionship.

AE IE YC YW FP NC AB FC SP CC WM WP WA

AE 1 0.43 0.28 0.36 0.09 0.58* 0.23 -0.08 0.26 -0.05 -0.15 -0.06 -0.19

IE 0.43 1 -0.13 0.12 -0.26 0.15 0.11 -0.01 0.29 0.16 -0.00 -0.23 -0.05

YC 0.28 -0.13 1 0.42 0.06 0.40 -0.07 0.24 0.23 -0.09 -0.30 -0.28 -0.05

YW 0.36 0.12 0.42 1 -0.06 0.33 -0.21 0.00 0.12 -0.12 -0.15 0.03 -0.06

FP 0.09 -0.26 0.06 -0.06 1 0.07 0.00 0.33 0.03 -0.24 -0.18 -0.27 -0.09

NC 0.58* 0.15 0.40 0.33 0.07 1 0.01 0.06 0.04 0.04 -0.06 -0.09 0.00

AB 0.23 0.11 -0.07 -0.21 0.01 0.01 1 -0.19 0.04 -0.08 0.05 -0.12 -0.18

FC -0.08 -0.01 0.24 0.00 0.33 0.06 -0.19 1 -0.05 -0.07 0.08 0.21 0.10

SP 0.26 0.29 0.23 0.12 0.03 0.04 0.04 -0.05 1 0.09 -0.20 -0.00 -0.09

CC -0.15 0.16 -0.09 -0.12 -0.24 0.04 -0.08 -0.07 0.09 1 0.02 0.29 0.36

WM -0.15 -0.00 -0.30 -0.15 -0.18 -0.06 0.05 0.08 -0.20 0.02 1 0.49 0.30

WP -0.06 0.23 -0.23 0.03 -0.27 -0.09 -0.12 0.21 -0.00 0.29 0.49 1 0.58*

WA -0.19 -0.05 -0.05 -0.06 -0.01 0.00 -0.18 0.10 -0.09 0.36 0.30 0.58* 1

*indicates a relatively higher correlation.



Soon Ae Chun is an Associate Professor in Business Department, and a member of the doc-

toral faculty of Computer Science Program, at the City University of New York, College of

Staten Island. Her research areas include database, information security and privacy, applied

informatics, such as Digital Government, e-business and e-Health; semantic approaches for

Workflow and Web services; Web 2.0 and mobile Web. She received her Ph.D.. in Information

Technology from Rutgers University, and M.S. in Computer Science from SUNY Buffalo. She

is a member of IEEE and the ACM. Contact her at [email protected]

Yoo Jung An Dr. Yoo Jung An received M.S. and Ph.D. degrees in Computer Science from

New Jersey Institute of Technology (NJIT) in January 2004 and 2008, respectively. Currently,

she is teaching IS and CS courses at Fairleigh Dickinson University (FDU) and NJIT as an

adjunct professor. Her research interests include Semantic Web, Health Informatics, Deep Web,

Data Warehouses and Artificial Intelligence.

Sunju Park is an Associate Professor in School of Business at Yonsei University in Korea.

Her research interests are the application of OR techniques to real-world problems, such as

online social networks, smart grid, healthcare, and telecommunications. She received her Ph.D.

in Computer Science and Engineering from University of Michigan, and M.S. and B.S. in Com-

puter Engineering from Seoul National University. Contact her at [email protected].

June-Suh Cho holds Ph.D. from Rutgers University, M.S. from New York University and B.A.

from KyungHee University. He is anassociate professor at Hankuk University's College of Busi-

ness Administration and Graduate School of Business. Dr. Cho had worked at several Invest-

ment Banks and Insurance Companies in New York City. He also had conducted research at

EC & Data Management group in IBM T.J. Watson Research Center. His research focuses on

e-Business, Multimedia Databases, Security, CRM, Mobile and Telecommunication technology.

He has published articles in IEEE, ACM, and other conferences and journals.

James Geller is a professor in the Department of Computer Science at the New Jersey Insti-

tute of Technology. His research areas include the structure and semantics of object-oriented

databases; Semantic Web search and mining, and the Unified Medical Language System, where

he and his colleagues have taken a leading role using regularities in the structure of medical

ontologies to assure their correctness. His distinguished publication record in major medical

informatics conferences and journals was crowned by co-editing the groundbreaking special

issue on Auditing of Terminologies of the Journal of Biomedical Informatics in 2009. He has

received numerous awards, including: the Excellence in Research Award for NJIT's College of

Computing Sciences (2010); NJIT Excellence in Teaching Awards (2003, 2011); and the College

of Computing Sciences Teaching Award (2003). He was named a Master Teacher in 2005. Geller

received his PhD and MS from the State University of New York at Buffalo in 1988.

flexible payment recommender system - rutgers …cimic.rutgers.edu/~soon/papers/jita2011.pdf ·...

Documents