EVALUATION OF REPUTATION METRIC FOR THE B2C

e-COMMERCE REPUTATION SYSTEM

Anna Gutowska and Andrew Sloane

School of Computing and Information Technology, University of Wolverhampton

Wulfruna Street, Wolverhampton, U.K.

Keywords: Reputation System, B2C, e-Commerce, Simulation.

Abstract: This paper evaluates recently developed novel and comprehensive reputation metric designed for the

distributed multi-agent reputation system for the Business-to-Consumer (B2C) E-commerce applications.

To do that an agent-based simulation framework was implemented which models different types of

behaviours in the marketplace. The trustworthiness of different types of providers is investigated to

establish whether the simulation models behaviour of B2C E-commerce systems as they are expected to

behave in real life.

1 INTRODUCTION

The process of globalization creates new challenges

and opportunities for companies by offering an

access to new markets that were previously closed

due to cost, regulations etc. The adoption of the

Internet, in particular Internet-enabled B2C E-

business solutions, allows many Small and Medium

Enterprises (SMEs) to respond to these challenges

and opportunities by extending the geographic reach

of their operations. Very often however, Websites

created for sales purposes are simple in design and

functionality and therefore, do not arouse trust at

first glance. Furthermore, in contrast to “big brands”

which have already established their reputation in

the online marketplaces, SMEs are unknown to

many E-commerce customers.

In the E-commerce environment, which does not

require the physical presence of the participants,

there is a high level of “uncertainty” regarding the

reliability of the services, products, or providers.

Although many technologies exist to make the

transaction more secure, there is still the risk that the

unknown provider will not comply with the protocol

used. Thus, the decision of who to trust and with

whom to engage in a transaction becomes more

difficult and falls on the shoulders of the

individuals. In such an environment, reputation

systems come in place to assist consumers in

decision making. The basic idea is to let parties rate

each other to derive a trust or reputation rating. This

can assist in deciding whether or not to engage in a

transaction with this party. Reputation systems are

particularly useful in cases where the trustee is

unknown to the individual involved but well known

to others.

There are a number of existing consumer-to-

consumer (C2C) on-line reputation systems such as

eBay (2008) or Amazon (2008). However, unlike

C2C E-commerce marketplaces, most B2B sites do

not provide users with feedback information. There

are some centralized services/websites though,

which offer store ratings and reviews to their users,

such as BizRate (2008) or Resellerratings (2008).

All of them however, rely only on simple algorithms

calculating the average rating based on the given

feedback.

Nevertheless, much academic work on reputation

systems has been devoted to the C2C part of E-

commerce (Peer-to-Peer networks) which can be

found reviewed in (Sabater and Sierra, 2005; Josang

et al., 2007; Gutowska and Bechkoum, 2007a; Marti

and Garcia-Molina, 2006). Unlike the existing

centralized approaches (e.g. eBay, Amazon) which

are single-factor based, many authors proposed

distributed reputation systems which still tend to be

“one issue-centric” (Lin et al., 2005; Bamasak and

Zhang, 2005; Huynh et al., 2006; Fan et al., 2005)

(addressing only one of many problems existing in

the reputation systems (Josang et al., 2007;

Gutowska and Bechkoum, 2007a; Marti and Garcia-

489

Gutowska A. and Sloane A.

EVALUATION OF REPUTATION METRIC FOR THE B2C e-COMMERCE REPUTATION SYSTEM.

DOI: 10.5220/0001831104890498

In Proceedings of the Fifth International Conference on Web Information Systems and Technologies (WEBIST 2009), page

ISBN: 978-989-8111-81-4

Molina, 2006)). Even in studies attempting to

provide more complex reputation methods, for

example work on Histos/Sporas (Zacharia et al.,

2000), some issues are still not taken into

consideration, such as the transaction value, age of

rating, or credibility of referees.

Many of the problems addressed in C2C

reputation models also apply to the B2C E-

commerce environment. Not many authors however,

concentrate on the latter model of the marketplace.

The only work known to the authors addressing it is

(Cho et al.) and (Ekstrom and Bjornsson, 2002).

Nevertheless, to the best of the authors’ knowledge,

there are no studies whose main focus is to derive

reputation ratings in B2C E-commerce environment

taking into account the characteristics of the

providers.

This paper aims to evaluate a novel reputation

metric for computing reputation in a multi-agent

distributed B2C E-commerce system. To do that, the

agent-based simulation framework was

implemented. The strength of the metric is measured

by how well it reflects the agents (providers)

behaviour including resistance against different

hostile agents. Other important aspects of reputation

systems such as privacy of transaction data,

protection against collaboration attacks, and unfair

ratings are out of the scope of this work.

The proposed reputation metric evaluated in this

paper offers a comprehensive approach by including

age of rating, transaction value, credibility of

referees, and number of malicious incidents.

Furthermore, in addition to the information about

past behaviour it also incorporates other aspects

affecting online trust which are based on providers’

characteristics. Past behaviour, is not the only

information source affecting trust/reputation rating

of an online vendor. According to previous research

(Gutowska, 2007; Gutowska and Bechkoum,

2007b), there are many issues influencing online

trust-based decisions such as existence of trustmark

seals, payment intermediaries, privacy statements,

security/privacy strategies, purchase

protection/insurance, alternative dispute resolutions

as well as existence of first party information. The

extended approach evaluated in this paper yields a

promising improved distributed B2C reputation

mechanism. The problem of complexity of the

reputation metric is further described in (Gutowska

and Buckley, 2008a; Gutowska et al., to appear).

2 REPUTATION SYSTEM

FRAMEWORK

The simulated reputation metric in this study is

designed for the B2C E-commerce reputation system

in which two main roles are considered: buyer agent

i.e. agent representing a user and provider (web

service). A user agent collects for its user the

distributed reputation ratings about a web service

(provider). In return, a user provides the agent with

ratings about a transaction in order to build the

reputation database of the services. Agents create a

network where they exchange transaction ratings

about web services their users have dealt with, this

is called a buyers’ coalition in the paper. In this way

they are involved in a joint recommendation

process.

User agents and providers are engaged in a

transaction process e.g. buying-selling, where

money and products/services are involved.

To assess the reputation of a provider, first, a

user agent will use the information from the direct

interactions it has had with that party and second,

the ratings provided by other agents (indirect

ratings) from the buyers’ coalition which have dealt

with the provider.

The proposed reputation system is distributed

where each user agent will store their opinion about

transactions with other parties.

The assumption is that it is in users’ interest to

leave feedback after each transaction as that is the

only way the reputation system will work. The

participants are aware that if they want to calculate

the reputation rating about a particular provider the

only source of information will be their feedback

from the past and feedback received from other

users. Also, when entering the system as a new

member they are duty-bound to both. The users rate

transactions they were involved in and share this

information with others when requested.

3 REPUTATION METRIC

The reputation metric (Gutowska and Buckley,

2008a) evaluated in this paper is based on the

weighted average. The reputation value of provider

p is calculated as the arithmetic mean of the

compulsory reputation (Section 3.1) and the optional

reputation (Section 3.2). In addition the weight

wm(p) based on the number of malicious incidents is

applied.

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490

If optional reputation metric is not calculated

then the reputation metric takes the value of

compulsory reputation metric multiplied by wm(p).

Further, the full rating scale of trust is [0; 1].

3.1 Compulsory Reputation

Compulsory reputation is based on the set of the

compulsory parameters and is defined as the

arithmetic mean of aggregated direct and indirect

ratings (see below). The rating scale for compulsory

reputation metric is [0; 1].

3.1.1 Compulsory Parameters

Transaction Ratings. In the proposed system the

quality factors constitute the explicit ratings left by

the user after the transaction and consist of three

components (as vector values): transaction outcome

i.e. if the product/service was received, fulfilling

provider’s signals (Rein, 2005) e.g. if the delivery

time, the product were as promised, as well as

customer service/support.

Raters’ Credibility (Implicit User Reputation).

Whilst choosing the group of users to require the

data from to calculate indirect reputation, it is

important to take their credibility as referees into

account. The reason for that is three-fold. Firstly, it

is often too costly or impossible to collect ratings

results from all interactions with the provider in

question (Josang et al., 2007). Secondly, to avoid the

inclusion of dishonest feedback into reputation

calculations from users demonstrating colluding

behaviour or leaving unfair ratings. Thirdly, to

choose the right subset of users with “similar

opinions”. Namely, different people have different

standards and they tend to trust the opinions of

people who have the same standards with

themselves (Zacharia et al., 2000). The solution

applied here is to extract users’ reputation

automatically and implicitly from their past

transaction rating data and use it to choose “n

best/most suitable raters”. The method presented

here is inspired by (Cho et al.) and uses raters’

ratings to estimate their underlying credibility. It is

based on the source credibility theory (Best et al.,

2003) which employs several schemes of

collaborative filtering methods (using similarities

between a target rater and the rest of the users). The

theory was shown to support rating mechanisms

both in the B2B (Ekstrom and Bjornsson, 2002) and

B2C (Cho et al.) E-commerce.

Source of Feedback. The reputation metric in this

study applies the weight ws(p) based on the rating

tendency concept proposed in (Cho et al.). It

decreases the rating from the rater who has a

tendency to rate higher than others, and vice versa.

)(1)(

)(uAu

gguws −−=

(1)

Where:

is the average transaction ratings from a rater u

A(u)

is the average ratings of the other users from

the subset of the “best/most suitable users”

(for the providers that the rater u rated).

Reputation Lifetime. In order to model the

dynamic nature of reputation, the weight associated

with the reputation lifetime wt is applied which

constitutes an exponential function of time. In this

way the more recent ratings are considered more

important and are valued higher comparing to the

older ones. Furthermore, as in (Zacharia et al.,

2000), the memory of the reputation system is

considered which disregards very old ratings.

)

(2)

( xt

Δ−

Where, ∆t(x) is the time difference between the

current time (i.e. time of request) and the time when

the transaction x took place. β is used to scale ∆t(x)

and β > 1. The time weight is applied to the

reputation metric in a recursive algorithm (Section

3.1.2).

Transaction Value. In counting reputation ratings

the value of the transactions is also taken into

account counteracting users who try to build a high

reputation by cooperating in many small transactions

and then cheat in a very large transaction. Also, the

transaction value range depends on the context to

which the reputation system will be applied i.e. the

maximum price of sold goods/services in the

marketplace. The weight associated with the

transaction value wv

is calculated using the

formula below:

)(

−

−=

and

→= xwhere

vMax

(3)

Where, v(x) is the value of transaction x and

vMax is the transaction range i.e. the maximum

value of the goods/services in the marketplace

(based on the context to which the reputation system

is applied). γ is used to scale v(x) and γ > 1.

EVALUATION OF REPUTATION METRIC FOR THE B2C e-COMMERCE REPUTATION SYSTEM

491

Number of Malicious Incidents. As in (Bamasak

and Zhang, 2005), in the proposed metric the

reputation value is reduced to the minimum when a

party reaches a certain threshold of malicious

incidents. Up to that threshold the appropriate

weight wm(p) is applied based on the exponential

function:

(4)

⎭

⎬

⎫

=≥

=<≤

−

0)(

)(0

pwmthenMmif

where

→= xwhere

(5)

Where, m is a number of malicious incidents of

provider p that occurred within the transactions

taken into calculation. M is the set threshold of the

number of malicious incidents above which the

reputation value is reduced to minimum. In the

equation above α is used to scale wm(p) and α > 1.

3.1.2 Computing Aggregated Ratings

The aggregated ratings are calculated with the

application of the recursive algorithm used on the

list of the transaction data records sorted according

to the time value.

The aggregated direct rating value is calculated

based on the data stored in the requesting agent a

database i.e. regarding its direct interactions:

[]

[

)(

)()()(

11)1(,

1,,

−−−

−

+⋅+

++⋅=

xxxxa

xxxxaxa

wtwtwtAGRD

wtwtwtpURpAGRD

]

(6)

For the case where x=0 the aggregated direct

rating is equal to the updated rating for that

transaction. Where UR

(p) is the updated rating

value of transaction j with provider p calculated by

agent a

and x is the index of the last transaction on

the list (n-1).

The aggregated indirect rating values are

calculated in the same manner as above but are

based on the list of the transaction data from the

subset of the “n best/most suitable users”. In

addition, the weight ws is applied for each user

providing information.

3.1.3 Computing Updated Ratings

Updated reputation rating UR

a,x

(p) is calculated by

agent a for transaction x in which a was involved

with provider p

In general, each provider is reputed

by an agent after each transaction by providing a

transaction rating g. This is the average of two

components: fulfilling provider’s signals and

customer service, where both can take values [0; 1].

In addition, appropriate weight wv based on the

transaction value is applied.

3.2 Optional Reputation

In addition to the parameters presented above, a user

may choose to include some or all of the optional

parameters into calculations, which will influence

the rating value of a provider. They are: existence of

trustmark seals, existence of payment

intermediaries, existence of first party information,

existence of privacy statements, existence of

security/privacy strategies, existence of purchase

protection/insurance, and existence of alternative

dispute resolution and are further described in

(Gutowska and Bechkoum, 2007a; Gutowska,

2007).

The optional reputation is based on the set of

optional parameters (providers’ characteristics)

which take values [0; 1] and is presented by the

average of the above parameters which have been

chosen to be included into calculation. The rating

scale for optional reputation metric is [0; 1].

Optional reputation constitutes the initial

reputation for newcomers as at that point there is no

information of the past behaviour available.

4 SIMULATING B2C

e-COMMERCE REPUTATION

SYSTEM

The reputation system simulator used in this study

was developed in Java and it is based on a slightly

modified version of the RePast agent-based

simulation toolkit (Schlosser, 2004).

In the presented simulation the market is

populated by a number of agents that are divided

into buyers and providers. The simulation is based

on discrete time ticks. At each tick buyer agents are

supposed to initiate a transaction with a provider and

rate him afterwards. After the agents finished their

actions the data is collected and represented

graphically.

In the simulation the agents may enter or leave

the community with equal probability (see

Simulation parameters in Section 4.5.).

4.1 Modeling the Buyers

The buyers in the simulation framework differ in

types. The buyer agent type is a combination of its

trust disposition and its expectations.

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492

Disposition to trust and the same risk attitude refer

to the fact that people have a baseline attitude when

they approach any trust situation. Some of them

have a higher baseline level of trust than others thus,

some individuals may find it easier/more difficult to

trust. The disposition to trust affects the decision of

either the buyer agent wants to engage in a

transaction with the provider or not (see the

acceptance function in Section 4.4.1.). Based on the

above there are different types of the buyer agents in

the simulation:

Risk Taking. This type of buyers is willing to take

risk easily which means they accept the high value

transactions even with the provider with low

reputation.

Very Cautious. This type of buyers is risk averse

and they are very careful with their decisions. They

accept the transactions only if the provider has high

reputation.

Conservative. Buyers representing this type come

between the two above extremes.

In the presented framework the buyer agents

have also different expectations towards the

outcome of the transaction which affects the way

they rate the transaction (see the rating function in

Section 4.4.2.). As in (Michalakopoulos and Fasli,

2005), there are three types of the buyers agents in

this study: optimists, realists, and pessimists.

Combining the two attributes discussed above

the following types of buyers agents were

implemented in the simulation framework: Risk

Taking Optimists, Risk Taking Realists, Risk

Taking Pessimists, Vary Cautious Optimists, Very

Cautious Realists, Very Cautious Pessimists,

Conservative Optimists, Conservative Realists, and

Conservative Pessimists.

4.2 Modeling the Providers

The effectiveness of a reputation system and its

metric depends on its resistance against malicious

behaviours. The success of non-honest agents is its

measurement for the quality of the metric (Schlosser

et al., 2005). Therefore there are different types of

providers implemented in the framework which are

called Trustworthy, Shady, Player, and Fly-By-

Night. They differ in their behaviour while

transacting (this is also correlated with their

characteristics). The characteristics of the interest

are the cheating probability (ChP) and the range of

the transaction outcomes they produce in terms of

customer service and fulfilling providers’ signals (in

other words the quality of services they provide).

The remaining attributes constitute the optional

parameters in the reputation metric and include:

existence of trustmark seals, payment

intermediaries, privacy statements, security/privacy

strategies, purchase protection/insurance, alternative

dispute resolutions as well as existence of first party

information. They have been chosen based on the

previously conducted survey discussed in

(Gutowska and Bechkoum, 2007a). The above

characteristics/optional parameters can take values

between 0 and 1 where 0 means no existence of the

attribute. In this way each type of the provider has

the optional reputation (OP) value based on the

above which constitutes the initial reputation value

for any new provider in the system. In the reputation

system there would be a devoted agent that would

gather the optional parameters information from the

providers’ Websites. The properties of different

providers are as follows:

Trustworthy. This type of the providers does not

cheat in the transactions (ChP=0) and provides high

service quality. All the parameters mentioned above

have high values (OP=0.92).

Shady. This agent does not have a particular pattern

in its behaviour (ChP=50). It provides false

statements on its Website which results in high

values of the optional parameters apart from

Trustmark Seals and Payment Intermediaries

(OP=0.63). The quality of the services it provides is

low.

Player. This type of a provider tries to build high

reputation by not cheating (ChP=0). When it

achieves its goal however, it starts behaving in a

malicious way (ChP:=100). When its reputation falls

down below the threshold then it starts being honest

again (ChP:=0). Player agent has got high values for

First Party Information, Privacy Statements and

Security Strategies (OP=0.43). When it does not

cheat the services provided are of a high quality.

Fly-By-Night. This agent’s goal is to cheat

(ChP=100). It provides false information about the

services it offers. The way of payment is direct to

the bank account (OP=0.51). The quality of the

services it provides is low.

4.3 The Simulation Cycle

The simulation framework is highly automated

where the handling of the agents, initiation of the

EVALUATION OF REPUTATION METRIC FOR THE B2C e-COMMERCE REPUTATION SYSTEM

493

transactions and storing the ratings are part of the

framework. The simulator repeatedly iterates a cycle

of events that would occur in the marketplace. The

steps of a transaction are as follows:

1. The simulation engine selects a buyer agent

who initiates a transaction with another provider

agent.

2. The buyer agent tests if the transaction is

acceptable i.e. he calculates the reputation of the

provider in question based on his previous direct

interactions as well as information from the

buyers community (the acceptance function is

described in Section 4.4.1.)

3. If the transaction takes place, the provider

agent determines the outcome of it and the buyer

agent rates it and stores the ratings. The ratings

depend on the buyer agent type and his

expectations and may not match exactly the real

outcome (the rating function is described in

Section 4.4.2.)

4.4 Modeling the Transaction

and Rating Processes

4.4.1 Transaction Acceptance Function

In the presented simulation the buyer agents have a

trust disposition which allows them to make

different decisions when it comes to engaging in a

transaction with a provider.

In this work the assumption is that no buyer

agent will transact voluntarily with a non-

trustworthy provider i.e. the provider with low

reputation. The other factor taken into consideration

while making the decision is the value of the

transaction. The acceptance function therefore, is a

correlation between the provider’s reputation and

the value of the transaction. The higher the value of

the transaction the higher the reputation should be

for the buyer to engage in this transaction. As

different people have different disposition to trust, in

the presented framework different types of buyer

agents have different acceptance functions. In this

way different types of agents accept the transaction

of a specific value at the different reputation level

Users’ willingness to trust however, can be

changed by experience (Shneiderman, 2000). In the

proposed framework all buyer agents representing a

specific type start with the same acceptance function

which is affected/changed later on by the outcome

of the transaction (experience) and in particular by

the providers’ malicious incidents. The calculation

of the acceptance threshold for a specific transaction

value with a specific provider is based on the

Lagrange Interpolation (Cheney and Kincaid, 1998).

4.4.2 The Rating Function

In the proposed framework each buyer agent rates

each transaction he has been involved in and collects

these ratings (see Transaction ratings Section 3.1.1)

in his database.

In a real marketplace, different people will rate a

transaction differently based on their experience and

their expectations towards the transaction outcome.

In the discussed simulation framework, three cases

are considered (as in (Michalakopoulos and Fasli,

2005)): optimists, realists, and pessimists. When it

comes to the transaction, optimists will be expecting

a very positive outcome, pessimists on the other

hand a rather bad outcome, and realists will come

somewhere between the two extremes. The

simulation framework addresses the above scenario

in a way that the optimist agent will hope for the

best outcome (in terms of customer service and

provider’s signals) he has had so far with the

provider in question, the pessimist agent will

anticipate the worst one, and the realist agent will

expect the average result based on his experience. If

the expected outcome (expOut) is higher than the

actual one (realOut), the buyer agent applies the

punishment value (p) to the transaction rating

(rating) which is a difference between the expected

and the real outcome value. If the expected outcome

value is equal or lower than the actual one, the

ratings reflect the outcome. The above rules are

presented below:

p:= expOut - realOut

if p>0 then

if p<= realOut then

rating:=realOut-Random(0, p)

else

rating:=realOut–Random(0,

realOut)

Apart from the transaction rating, the final

reputation value includes also the other component

which is Optional Reputation discussed in Section

3.2.

4.5 Simulation Parameters

There are several parameters which values can be

changed in the simulation framework depending on

the simulation needs. These are as follows: number

of starting agents (buyers and providers), number of

agents to add and remove in each step, add/remove

probability, the probability of initiating transaction

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494

at each tick by a buyer agent, the amount of agents

of each type in the community, and number of

parallel simulations.

There are also some parameters which affect the

reputation metric itself. These are the maximum

number of malicious incidents, the transaction value

range, weight for the source of feedback and weight

for time factor. The maximum number of malicious

incidents is a threshold above which the provider’s

reputation is decreased to zero. The transaction

value range depends on the context to which the

reputation system will be applied i.e. the maximum

price of sold goods/services in the marketplace. The

above values determine the weights for the time

factor and malicious incidents of the reputation

metric which are based on the exponential function.

The weight for the source of feedback scales the

importance of the ratings coming from the indirect

interactions (buyers’ community) and the weight for

the time factor determines the impact of old ratings

vs. the more recent ones. This weight is also based

on the exponential function and is applied to the

reputation metric in the form of a recursive

algorithm (more details on the proposed reputation

metric in (Gutowska and Buckley, 2008b; Gutowska

and Buckley, 2008a)).

5 EVALUATION CRITERIA

The strength of the metric is measured by how truly

it reflects the agents (providers) behaviour and in

particular by its resistance against different hostile

agents. In the simulation the average requested

reputation, the market honesty, the acceptance rate,

the average number of transactions and the average

number of malicious incidents are calculated

separately for each type of the provider agents.

Average requested reputation is the mean

value of all reputation ratings of providers from a

specific type as if calculated/received by a buyer

when requesting reputation rating. This is based on

the rating information stored in the buyers’

databases.

Market honesty is the mean value of the actual

outcomes from the transactions produced by the

provider agents (not ratings). These are stored in

providers’ databases.

Acceptance rate is the proportion of

accepted/completed transactions with all initiated

transactions with providers of a specific type.

Average number of transactions is the average

number of transaction that a provider of a specific

type was involved in (accepted transactions).

Average number of malicious incidents is the

average number of malicious incidents for a specific

type of a provider.

The horizontal axis in the Figures below

represents the time. In Figures 1, 2 and 6 the vertical

axis corresponds to the computed reputation, in

Figures 3, 4 and 7 it represents the number of

transactions and in Figure 5 the acceptance rate.

6 SIMULATION RESULTS

As there is no work known to the authors that

introduces the reputation metric for the B2C E-

commerce reputation system, this paper presents

pioneering results and it does not compare the

efficiency of the evaluated reputation metric with

any other.

Table 1 presents the parameters and their values

used in the simulation.

Table 1: Parameters used in simulation.

The simulation results are depicted in Figures 1-

5. Market Honesty (Figure 1) and Average

Requested Reputation (Figure 2) show that the

reputation metric correctly reflects the behaviour of

different types of providers i.e. Trustworthy agents

keep their high reputation scores throughout the

experiment and the different types of malicious

agents have low reputation due to their transaction

history. It is noticeable that initially the reputation of

the malicious agents is a bit higher and it decreases

with time. This is caused by the fact that the initial

EVALUATION OF REPUTATION METRIC FOR THE B2C e-COMMERCE REPUTATION SYSTEM

495

reputation for new providers with no transaction

records is their optional reputation which in many

cases is based on the false information provided by

them on their Websites. When the transaction

information comes into the equation however, the

reputation algorithm appropriately deals with the

scenario and decreases the reputation value.

Figure 1: Market honesty.

Figure 2: Average Requested Reputation.

The slight difference in values between Market

Honesty and Average Requested Reputation reflects

the fact that different types of buyer agents rate the

transactions differently which does not always

match the real outcomes. The dissimilarity however,

is not significant which strongly suggests that the

reputation metric closely mirrors the behaviours in

the marketplace.

The results shown in Figures 3-5 indicate that

malicious agents are not involved in many

transactions (Figure 3) due to their low reputation.

The Acceptance Rate (Figure 5) decreases as the

buyer agents do not accept transactions with the

providers with low reputation. The Average Number

of Malicious Incidents (Figure 4) is kept stable

which is controlled by the Maximum Number of

Malicious Incidents simulation parameter. If the

parameter is set as M=1 then the reputation metric

will decrease the reputation of this provider to 0

which means he will not be accepted as a transaction

partner anymore and it will not get a chance to gain

profit by cheating. This scenario is illustrated in

Figure 6, ceteris paribus (i.e. while other parameters

stay unchanged).

Figure 3: Average Number of Transactions.

Figure 4: Average Number of malicious Incidents.

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496

Figure 5: Acceptance Rate.

Figure 6: Average Number of Malicious Incidents, M=1,

ceteris paribus.

7 CONCLUSIONS

This paper presents and evaluates the novel,

comprehensive reputation metric designed for the

multi-agent distributed B2C E-commerce reputation

system. An agent-based simulation framework was

implemented that models the B2C E-commerce

marketplace. The results show that the proposed

reputation metric closely reflects different types of

behaviours in the marketplace. The method is

particularly resistant to malicious behaviour.

One of the assumptions of the proposed system

i.e., that there are no external parties included in the

framework can be easily amended in the future by

including the information coming from other

systems or reputation authorities. The other area

which could be looked at more closely is the

distribution of different buyer behaviours/types in

the real marketplace. The work on inclusion of those

in the proposed framework is underway.

REFERENCES

Am [01 July 2008].

Bam

vice, Hong Kong, 29 March -12

Bes

Building Marketing Strategy. McGraw-

Che cs

Cho

Systems with Applications, In Press, Corrected

eBa [01 July 2008].

Eks

ty.

Fan

s on

rmation Technology, University

lma de Mallorca, Spain, 29-31

ted Education, Dublin, 6-7 February,

azon (2008) [online].

<http://www.amazon.co.uk/>.

asak, O. & Zhang, A. N. (2005) A distributed

reputation management scheme for mobile agent-

based E-Commerce applications. In Proceedings:

IEEE International Conference on e-Technology, e-

Commerce and e-Ser

April,, pp. 270-275.

t, R., Corney, K. & Hawkins, D. (2003) Consumer

Behavior:

Hill/Irwin.

Bizrate (2008) [online]. <www.bizrate.com/>.

ney, W. & Kincaid, D. (1998) Numerical Mathemati

and Computing, International Thomson Publishing.

, J., Kwon, K. & Park, Y. Q-rater: A collaborative

reputation system based on source credibility theory.

Expert

Proof.

y (2008) [online].

<http://www.ebay.co.uk/>.

trom, M. & Bjornsson, H. (2002) A rating system for

AEC e-bidding that accounts for rater credibili

Proceedings of the CIB W65 Symposium, 753–766.

, M., Tan, Y. & Whinston, A. B. (2005) Evaluation

and design of online cooperative feedback mechanism

for reputation management. IEEE Transaction

Knowledge and Data Engineering, 17, 244-254.

towska, A. (2007) Research in Online Trust: Trust

Taxonomy as A Multi-Dimensional Model. School of

Computind and Info

of Wolverhampton.

towska, A. & Bechkoum, K. (2007a) A Distributed

Agent-based Reputation Framework Enhancing Trust

in e-Commerce. In Proceedings: IASTED

International Conference on Artificial Intelligence and

Soft Computing, Pa

August, pp. 92-101.

towska, A. & Bechkoum, K. (2007b) The Issue of

Online Trust and Its Impact on International

Curriculum Design. In Proceedings: The Third China-

Europe International Symposium on Software

Industry-Orien

pp. 134-140.

towska, A. & Buckley, K. (2008a) A Computational

Distributed Reputation Model for B2C E-commerce.

In Proceedings: IEEE/WIC/ACM International

EVALUATION OF REPUTATION METRIC FOR THE B2C e-COMMERCE REPUTATION SYSTEM

497

ational Conference on Distributed Computing

Gu ,

Special Issue on Trust and

nce on

Jos urvey of trust

Lin

Ma H. (2006) Taxonomy of trust:

Mi M.

Rei

l Hawaii

p. 26a.

Sabater, J. & Sierra, C. (2005) Review on Computational

A. dt (2004)

cumentation [online].

Sch n the

Zac Collaborative

reputation mechanisms for electronic marketplaces.

Decision Support Systems, 29, 371-388.

Conference on Intelligent Agent Technology

Workshops, 9 December, Sydney, Australia, pp. 72-76.

towska, A. & Buckley, K. (2008b) Computing

Reputation Metric in Multi-agent E-commerce

Reputation System. In Proceedings: IEEE

Intern

Systems Workshops, 20 June, Beijing, China, pp. 255-

260.

towska, A. Sloane, A. & Buckley, K. (to appear) On

Desideratum for B2C E-commerce Reputation

Systems. Journal of Computer Science and

Technology. The

Reputation Management in Future Computing Systems

and Applications.

ynh, T. D., Jennings, N. R. & Shadbolt, N. R. (2006)

Certified reputation: how an agent can trust a stranger.

In Proceedings: International Confere

Autonomous Agents and Multi-Agent Systems,

Hakodate, Japan, 8-12 May, pp. 1217-1224.

ang, A., Ismail, R. & Boyd, C. (2007) A s

and reputation systems for online servise provision.

Decision Support Systems, 43, 618-644.

, K. J., Lu, H., Yu, T. & Tai, C.-e. (2005) A reputation

and trust management broker framework for Web

applications. In Proceedings: IEEE Internationa

Conference on e-Technology, e-Commerce and e-

Service, Hong Kong, 29 March-1 April, pp. 262-269.

rti, S. & Garcia-Molina,

Categorizing P2P reputation systems. Computer

Networks, 50, 472-484.

chalakopoulos, & Fasli, M. (2005) On Different

Trust Attitudes and Their Effects on the Electronic

Marketplace. IEEE/WIC/ACM International

Conference on Intelligent Agent Technology, 102-108.

n, G. L. (2005) Reputation information systems: A

reference model. In Proceedings: Annua

International Conference on System Sciences, Big

Island, Hawaii, USA, 1-6 January, p

Resellerratings (2008) [online].

http://www.resellerratings.com/>.

Trust and Reputation Models. Artificial Intelligence

Review, 24, 33-60.

Schlosser, Technische Universitat Darmsta

Reputation System Simulator Do

<http://www.inferenzsysteme.informatik.tu-

darmstadt.de/users/schlosser/>.

losser, A., Voss, M. & Brückner, L. (2005) O

Simulation of Global Reputation Systems. Journal of

Artificial Societies and Social Simulation, 9, 4.

Shneiderman, B. (2000) Designing trust into online

experiences. Communication of the ACM, 43, 57-59.

haria, G., Moukas, A. & Maes, P. (2000)

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