This document summarizes a seminar paper on reputation systems. The paper discusses how reputation originally emerged in social sciences but is now important for online networks and marketplaces. It defines key concepts like agents, evaluators, targets, and beneficiaries. The paper also outlines threats to reputation systems like ballot stuffing, bad mouthing, and herd behavior. It examines systems where evaluators, targets, and beneficiaries overlap and discusses using reputation to infer trust in decentralized networks. It also explores using reputation in marketplace simulations where buyers and sellers have different motivations.

1. Seminararbeit
Seminar on Service-oriented Computing
WS-2009/2010
Reputation Systems
Richard Metzler
r.metzler@paadee.com
March 2010
betreut durch Rehab Alnemr

2. Reputation is a term referring to a distributed social knowledge about agents in a
networking system that enables trust between agents not knowing each other before. It is
originally used by social sciences, but as distributed computer networks, the internet and
social networking sites are growing in numbers and in importance, the value of reputation
increases and therefor the need for good abstractions of reputation is growing. Big
websites like Google, Amazon, eBay and Facebook implement reputation based systems,
but there are much more sites where reputation isn’t as evident. The Internet of Services
(IoS) - an idea that is fueled by the Service Oriented Architecture (SOA) paradigm -
should also profit from reputation based systems. This paper gives an overview about
reputation systems and discusses some threats to these systems.

3. 1 Introduction
Reputation is a social-psychological mechanism that enables trust between partners not
knowing each other before. [3] For centuries reputation has been a social construct that
existed in all human societies. Partner selection, social control and coalition formation
are some of the main functions of reputation. [1] Before the internet era reputation was
limited by geography and social boundaries. Only the biggest companies had a globally
known reputation. Now it is possible for very small businesses to compete in a global
market and have a global reputation, thanks to global e-markets such as eBay. But also
the Internet of Services (IoS) will profit from reputation systems as these systems will
ensure quality even in fast changing environments.
Building a good reputation and having access to accurate reputation information pro-
vides a lot of economic benefits. This is especially valid in competitive markets where
many sellers each having a small marketshare and products are only slightly different so
the buyer’s decision depends mostly on the seller’s reputation. [1]
Reputation is specific to a context. For example, a book critic who reviews literature
may not be a good advisor when you want to buy a car on a used car market. In
reputation based systems the reputation of an agent is also mostly specific to the system
and may be specific to the different roles the agent can act in.
Trust is linked to reputation and depends on how reputation is modelled specific to the
reputation system. By introducing ratings, aggregating reputation and providing easy
access to this information it is possible to lower the proportion of fraudulent activities.
This is essential for e-markets such as eBay in order to create consumer trust in it’s
reputation systems.
The value of online reputation will continue to increase. While this will encourage
honest behaviour in order to benefit from a high reputation, at the same time it makes
reputation an asset which appeals to dishonest entities and hence a target for attack. [3]
In section 1.1 we will describe all properties related to reputation systems and the
entities within.
1.1 Definitions
Entities which are able to perform actions autonomously in a given context are called
agents. In any reputation system there are three sets of agents and entities [1] :
• Those who share the evaluation. They are called evaluators.
• The targets which are evaluated.
• The beneficiaries of the evaluation.
In addition, reputation systems often involve third party agents or gossipers who trans-
mit reputation information but are not responsible for the evaluation.
It is important to mention that these sets can overlap in a reputation system. We
distinguish between two significant cases:
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4. 1. Evaluators, targets and beneficiaries overlap in general.
2. Evaluators and beneficiaries overlap but targets differ from these sets.
According to [1] image is the output of the process of evaluation of an agent whereas
reputation expresses a collective opinion and may be aggregated from the opions of
multiple agents. Image is assumed to be true by the agent that holds it; reputation is the
voice the agent considers spreading and is not necessarily true. Image always requires
one identified evaluator, whereas reputation is commonly shared knowledge without an
identified source. Reputation is subject to change (especially as an effect of corruption,
errors, deception, etc.) and it emerges as an effect of a multi-level bidirectional process.
[4]
Therefor an agent’s first hand experience (image) should weight more than trusted
advisor’s first hand experiences, whereas trusted advisor’s experiences may outweight
global reputation to a certain degree. Time and value of transactions as well as other
values specific to the reputation system should also be factored in.
As presented in [3] an important component in any reputation-based system is an in-
centive / punishment scheme. An agent should be encouraged to behave in a trustworthy
manner in order to acquire, manintain and increase its reputation and benefit from it.
At the same time it is needed to discourage misbehaviour by appropriate punishment
reaching from lowering reputation to banning an agent from a community.
The main structure of reputation systems can be centralised or decentralised. In cen-
tralised reputation systems a central server manages reputation scores for targets while
in decentralised systems reputation is transferred only between nodes.
After having defined the terms we are going to use, we are going on to discuss some
of the major threats to reputation systems in section 2. Then we discuss reputation
systems with overlapping sets of evaluators, targets and beneficiaries in section 3 and
marketplace simulations as one example for reputation systems with targets and evalu-
ators not overlapping in section 4.
2 Threats for Reputation Systems
There are different kinds of threats towards reputation-based systems. Some threats
are directed towards the underlying network infrastructure or are related to identy and
identity changes. In order to provide most accurate reputation scores the implementers
of a reputation system should secure the system as much as possible but should be aware
that there have to be tradeoffs if the system should be useful. This is especially valid
in reputation systems where agents are humans who need to easily identify, understand
and rely on reputation ratings.
This paper will focus on other types of threats such as unfair ratings and social threats.
To improve trust in reputation systems and lower proportion of fraudulent activities, we
have to understand these threats.
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5. 2.1 Ballot stuffing and bad mouthing
One of the most common threats towards reputation systems is an agent that provides
false reputation scores. In this context the term ballot stuffing refers to an evaluator that
transmits more positive ratings for the target to quickly grow reputation.
Bad mouthing refers to the opposite where the attacker transmits negative feedback
for the target in order to lower it’s reputation.
The effectiveness of both attacks is improved by sybils or a number of attackers col-
luding. Sometimes it is also combined with flooding where the evaluator transmits many
ratings for the same target in a short time period. While flooding is mitigated easily by
counting only one rating per time window, sybils and colluding attackers may have to be
identified by the underlying network or by special detection algorithms.
2.2 Recommender’s Dishonesty
Because reputation scores are independet from the evaluator, reported reputation is
strongly dependent on the trustworthiness of the voter providing reputation feedback.
Recommender’s dishonesty may lead to fraud through a malicious third agent. One sug-
gestion to mitigate this threat is to introduce weightings to a reported reputation score,
according to the reputation of the recommender (voter). Another mitigation technique
uses only recommenders from a trusted social network.
2.3 Risk of Herd Behaviour
In some reputation systems a majority of voters may outvote a minority. Because in-
novative or controversial opinions are often expressed by minorities, these minorities
may be outvoted and gain a bad reputation from thinking differently. This penalisation
may reinforce what the majority thinks, hinder society progress and cause risk of herd
behaviour.
In order to mitigate this thread the reputation system may provide more anonymity
to voters. Also personalised reputations could be introduced so that a controversial
user might have a low computed global reputation rating but might have a very high
reputation rating in the eyes of a restricted number of users.
2.4 Discriminatory Behaviour
In second-order reputation systems where recommenders are weighted by the their trust-
worthiness, an entity can choose to cooperate only with peers who have a high reputation
rating and defraud those who have a low reputation. Through this discriminatory be-
haviour the entitie’s reputation scores highly because the recommendations of entities
with high reputation are weighted more heavily.
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6. 3 Reputation Systems with One Single Type of Agents
In reputation based systems with one single type of agents, the sets for evaluators, targets
and beneficiaries generally overlap.
Examples for these systems can be found in the “web of trust” and in many other
reputation systems. Often there is an underlying network with one single type of nodes
that can be rendered as an directed graph. Examples for this are webpages linking to
each other, BitTorrent networks or the social website Twitter where an directed edge
represents a follower relationship.
The following section 3.1 discusses an attempt on inferring reputation of a node not
known directly but through a chain of directly known nodes.
3.1 Inferring Reputation an the Semantic Web
The inferring algorithm proposed in [5] works on a directed graph with reputation ratings
attached to the edges and can be used in a decentralised reputation system. A directed
edge from node A to node B indicates B’s reputation rating for A. The rating scale
only consists of the discrete values 1 and 0 where 1 means “good” and 0 means “bad”
reputation. Node A trusts all nodes for which it has a reputation rating of 1 and distrusts
all nodes with a reputation score of 0.
The algorithm tries to infer a reputation score for nodes that are not directly known
to the evaluator but are known in the extended network of all nodes trusting each other.
The algorithm assumes the existing of one path of connected nodes between the evaluator
E and the target T.
If an evaluator E needs the reputation rating for a target node T and allready is an
adjacent neighbor of T than the attached value at the edge is the reputation score of T. If
T is not directly known to E, E starts by polling his trusted neighbors with a reputation
score of 1. Nodes with bad reputation are ignored as they are untrusted and considered to
return unreliable information and to be potentially malicious. Whenever a node receives
an evaluation request and has a direct reputation rating for T it will transmit this rating.
Otherwise it polls all his trusted neighbors and compute the average of their ratings and
finally return a rounded reputation rating. A value between 0.5 and 1 rounds up to 1
while anything under 0.5 will round down to 0. This way nodes in the path between the
evaluator and the target work as gossipers who transmit reputation.
Nodes that are in more than one path are polled according to the number of pathes.
Because they are polled by more than one other node they have to be considered more
trustworthy. In order to prevent unnecessary polling, the computed rating will be cached.
Because of the rounding mechanism the polling node essentially takes a majority vote
from it’s trusted neighbors. If half or more neighbors say that the target has a good
reputation the average is 0.5 or more, which rounds up to 1.
3.1.1 Malicious Nodes and Errors
The paper considers the worst case when malicious nodes are always returning incorrect
reputation ratings - they turn every rating into it’s opposite rating. Additionally good
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7. nodes may make errors in ratings with a certain probability.
If a node incorrectly gives a good node a bad rating chances are good that this rating
will be ignored because it needs only one other good node to outvote this bad rating.
Hence the paper shows that the probability of making an accurate decision is increased
by the level of indirection and the count of different pathes. The inferring mechanism in
the directed graph will increase the accuracy of reputation ratings.
The situation is much more serious if a good node incorrectly gives a bad node a good
rating. The bad node will lie about all nodes, bad mouthing every good node and ballot
stuffing all bad nodes. Subsequently the victim node V may add more and more bad
nodes to it’s trusted network. Additionally all nodes that rely on V’s correct ratings may
add bad nodes to their own trusted network.
The algorithm can be generalized by also polling untrusted neighbors and weight the
submitted rating by the neighbor’s reputation score. Without rounding it would also be
possible to use a higher threshold than 0.5 . This may be advantagous as [1] states that
human buyers in e-markets look for reputation ratings of 0.9 and higher.
It should also be considered to count the numbers of adjacent neighbors for target T
or nodes in the path or nodes overall. It has to have a meaning, because as shown in
the paper the mathematical error of inaccurate rating will decrease with the number of
nodes.
4 Marketplace Simulations
Marketplace simulations usually consists of two kinds of agents: Buyers have to find
products with a small price and good quality while sellers try to maximize profits by
increasing price and numbers of products they sell. These contradictionary motivations
lead to interesting competitive situations as the amount of products traded per round
and in total is usually limited by stock, money or other constraints.
Buyers have to estimate the trustworthiness of sellers and quality of products in order
to make good bargain. By enabling buyers to rate sellers and make these ratings available
for other buyers, a reputation system is established wherein buyers are evaluators and
beneficiaries and sellers are targets. In some marketplace simulations sellers can choose to
alter the quality of priced and non-priced features or choose to not deliver the product at
all. Such behaviour has to have an impact on the seller’s reputation and should generally
lead to fewer future collaborations as a punishment. In reputation based marketplaces
it is expected that buyers should find more trustworthy sellers who sell better products
at a lower price.
In the following sections the author discusses two different approaches for implementing
agents in reputation systems for marketplace models. In section 4.1 a strict mathemanti-
cal approach is described and in section 4.2 and the following sections the RePage model
- a cognitive inspired reputation model for agents - is introduced and two papers based
on RePage are discussed.
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8. 4.1 A Trust-Based Incentive Mechanism for E-Marketplaces
In [6] the marketplace is modeled as a reverse auction platform where buyers describe
what they want to buy and sellers bid in these auctions. Additionally sellers may act as
gossipers in an advisor role when chosen by another seller as neighbor. This neighbor
relation is asymmetric and the number of neighbors any seller can have is fixed.
Traded products have a price and non-price features like warranty or delivery time.
These features are weighted in the auctioning process and the buyer decides for the
product with best price, features and seller’s reputation.
The reputation system is centralized and a central server manages all transaction and
advisor ratings as well as the list of neighbors for every buyer. To rate a seller the buyer
is only able to submit a rating of 1 if the product is delivered with all features or 0 as a
negative rating when the product misses some features or is not delivered at all.
To start an auction, the buyer sends an “buy request” and matching “evaluation criteria”
for the desired product. The evaluation criteria consist of non-priced features that are
important to the buyer and the weightings for each feature.
In order to avoid fraud by dishonest sellers, buyers allow only sellers that are consid-
ered trustworthy to participate in that buyer’s auction. To model the trustworthiness
of a seller the weighted image and reputation values are combined. The public repu-
tation is derived from the neighbors’ ratings for the seller weighted by the neighbors’
trustworthiness. Additionally a forgetting rate is factored into the value to weight recent
transactions higher than older ones.
The seller will only considered trustworthy if it’s reputation value is equal to or larger
than a certain threshold. Only these trustworthy sellers are allowed to register for the
auction. An exception from the rule will be made if there aren’t any sellers with trust
values higher than the threshold. All sellers allowed to participate in the auction than
submit their bids for price and corresponding non-price features and the buyer determines
the winner of the auction.
The winning seller may decide to act dishonest in this transaction by altering the
quality of the delivered product or not deliver it at all. The only security mechanism the
buyer has against fraud is the modelling of the seller’s trustworthiness beforehand and
submit a rating after transaction.
Buyers model their private reputation of other buyers as advisors by comparing the
ratings for commonly rated sellers to their own ratings according to the elemental time
window when the transaction happend. The private reputation is estimated as the ex-
pected value of the probability that advisor A will provide reliable ratings to buyer B.
When there are not enough rating pairs to determine the private reputation, advisor A’s
public reputation will also be considered. To model the public reputation of an advisor
it is assumed that a seller’s behaviour does not alter in an elemental time window.
Hence, the last transaction rating from each buyer in every elemental time window is
used in a majority voting to determine the trustworthiness of the seller for this time
window. Ratings consistend with the majority are considered fair ratings. The greater the
percentage of fair ratings advisor A provides, the more reputable it will be. Private and
public reputation are then weighted by the reliability of the estimated private reputation
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9. value and combined. This heuristic may cause risk of herd behaviour if buyers know how
other buyers vote in the same elemental time window.
Sellers don’t have a fixed price for a product but are able to set a different price in
every auction. Because reputable buyers can recommend sellers to many other buyers,
this will increase the likelihood of future profit in case of collaboration. Hence sellers
give discounts to these reputable buyers by modelling the buyer’s reputation as advisor
by counting the numbers of other buyers who list the buyer as their neighbor.
Buyers who provide fair ratings (ratings that are consistent with the majority of ratings
for a seller) are considered trustworthy and are more likely choosen as advisors than other
buyers. Because sellers are able to model the reputation of buyers by counting how many
buyers consider the concerned buyer as an advisor, they can offer discounts to influential
advisors. This is an incentive mechanism for buyers to provide fair ratings and become
an advisor for as many buyers as possible.
The authors did several experiments and confirmed these facts in their results [6] :
• honesty is more profitable for both buyers & sellers
• sellers are more profitable when modeling reputation of buyers
• buyers are more profitable when they provide “fair” ratings
• buyers derive better profit when the use ratings of other sellers and measure trust-
worthiness of other buyers
Unfortunately the authors didn’t describe any experiment with sellers that model
the buyer’s reputation and use discriminatory behaviour towards buyers without any
influence as advisor. A dishonest seller may cooperate only with highly reputated buyers
and deceive non reputated buyers. If the seller act dishonest in less than 50% of all
transactions per time window the majority vote may show that he is a good seller and
the deceived buyers provide “unfair” ratings. Also it is unclear if unfair buyers model the
private reputation of other buyers after their real image or their transmitted reputation
as only transmitted reputation is stored on a central server.
4.2 The RePage Model
The RePage model [2] [4] provides a method for agents to compute the reputation of
targets through a cognitive model.
It’s memory consists of 5 layers of interconnected predicates with image and reputation
at the highest level. Each predicate contains a probabilistic evaluation that refers to a
certain agent in a specific role. For instance, an agent may have an image of agent T
(target) as a seller (role) and a different image of the same agent T as informant. The
probabilistic evaluation consist of a probability distribution over the discrete sorted set
of labels: Very Bad, Bad, Normal, Good, Very Good. [4]
The interconnection of predicates is responsible for aggregating and updating of all
predicates in order to create predicates with higher strength out of predicates with low
strength. For instance predicate Outcome is generated out of predicates Contract and
Fulfillment, and represent the agents subjective evaluation of direct interaction.
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10. 4.3 Reputation for Innovating Social Networks
In [4] Conte et al use the RePage model for agents. The paper discribes a marketplace
simulation where buyers not only rate sellers but also rate other sellers in informer role
for providing information. Additionally the paper investigates if the use of reputation in
decision processes has benefits over transmitting only the image.
In this e-market model products have a quality value {1,100} and the buyer creates
his image of the seller out of this quality value. Per round every buyer picks the seller
with best image or reputation score above a certain threshold. There is a limited chance
to explore unknown sellers of 1 / (knownSellers + 1), meaning in first round a buyer has
to choose a seller unknown to him and in second round there is a 50% chance of choosing
an unknown seller.
Sellers have a limited stock of products and disappear when stock is exhausted. A
new seller with similar properties enters the market. This new seller is yet unknown to
the buyers and needs to build a reputation for himself in order to gain any marketshare.
This makes sellers with good quality products a scarce resource. The paper researches if
clients perform better by sharing or by hiding reputation information about good sellers.
Per round a buyer may ask another buyer as a gossiper in informer role one out of
two possible questions. Informers are chosen exactly the same way sellers are chosen.
Question Q1 is “How honest is buyer X as informer?” and Q2 is “Who is a good seller, who
is a bad seller?”. Reputation information is transmitted as answers to these questions. An
informing buyer may cheat and answer by transmitting the opposite value. Retaliation is
enabled through sending “I-don’t-know” as answer. An informer may send this when he
had received false or inaccurate information from the asking buyer before. An informer
may also send “I-don’t-know” if he fear retaliation for possible inaccurate information.
The authors did several experiments with buyers transmitting only their image (L1) or
transmitting image and reputation (L2). They found that image and reputation based
systems perform better than only image based systems. This is especially true when
there are only a few good sellers in the market due to the larger amount of information
circulation in L2. But they also found that both systems L1 and L2 converge when the
amount of good sellers increase. Unfortunately Conte et al didn’t show any results for
experiments with different rates of cheaters.
4.4 Using the RePart Simulator to Analyze Different Reputation-Based
Partnership Formation Strategies within a Marketplace Scenario
The paper [2] by Avegliano and Sichman uses the RePage model and describes several
experiments to determine advantages and disadvantages of various strategies for agents
taking part in this marketplace simulation. It is examined how manipulating information
can effect the reputation of sellers and hence the amount of sold products.
Buying and selling agents - the paper uses the terms consumer and enterprise - col-
laborate in a reputation based marketplace. There are 3 different types of buyers and 7
different types of sellers.
Buyers use different strategies to determine which product to buy: Conservative buyers
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11. more partners less partners
same price, same quality Reactive (default)
increase price Greedy
decrease price Wholesaler Strategist
increase quality Investor
decrease quality Cheater Decadent
Table 1: price and quality oriented selling agent profiles
choose the seller with best image or reputation; Bold buying agents choose the seller with
the best relation of image/reputation devided by the price while a Miser buyer chooses
the product with the lowest price. Avegliano and Sichman found conservative buyers got
the products with the best quality but had to pay the highest price. Bold and miser
buyers payed roughly the same price but the quality of products bought by bold agents
were significant higher.
Sellers employ quality and price related strategies as listed in table 1. In order to
compare performance of seller profiles the Reactive strategy - never change price nor
quality - was employed as default seller profile. The authors found that selling agents
employing Investor strategy performed best of all quality oriented strategies and Reactive
profile performed better than Cheater and Decadent which both had similar outcome.
Out of the price oriented selling profiles Greedy performed better than Reactive, while
Strategist and Wholesaler both performed worse. This shows that a seller should invest
into quality of products instead of trying to sell cheaper than competiting sellers.
The authors tried to experimentally determine the influence of rumors to the system.
When a rumor is inserted into the reputation system the affected buyer has to repass it
to all neighbor consumers, broadcasting the reputation information to as many potential
buyers as possible. The authors found that inserting false positive and false negative
rumors (Ballot stuffing and Badmouthing) into the reputation system had only small
and short effects for the affected seller’s reputation. But inserting true positive rumors
effectively gained a faster rise and higher marketshare for the affected seller.
This said, the fast transmission of true reputation information seems to be very valuable
for buyers and for the concerned seller who will gain good a good reputation from good
quality. If agents can rely on reputation information employing a bold strategy will pay
off for buyers.
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12. 5 Conclusions and Future Work
Sharing reputation information and having fast access to it enables agents to make
smarter decisions. Reputation systems will continue to grow in relevance including but
not limited to the Internet of Services where more and more webservices start to compete
for assignments. The instant access to reputation will enable buyers to act bold, ensuring
quality while keeping the price at a minimum.
In order to make this possible reputation information should be transmitted as fast
as possible and even should be transmitted when it may be inaccurate. As more agents
submit ratings, the mathematical error of inaccurate ratings will decrease.
Sellers should invest in quality of products because reputation is mostly derived thereof.
This strategy will pay off as sellers can act greedy when quality is ensured. As we learned
sellers are more profitable when modeling reputation of buyers and giving discounts to
influential buyers to promote the seller in the buyer’s network.
Additionally gossipers and evaluators should be rewarded when providing fair ratings
as honesty is more profitable for both buying and selling agents and punished for dishon-
esty but without having to fear punishment for small errors. This can happen through
sellers modeling reputation of buyers or through other mechanisms. Additionally agents
should rate trustworthiness of third party agents who provide reputation information in
order to limit the influence of cheaters and weight information by trustworthiness of the
transmitting agent. A forgetting rate specific to the application should be factored in as
agents might change strategy or become smarter and more accurate over time.
Cheating in reputation systems has only limited effect. Ballot stuffing and Bad-
mouthing other agents may only lead to bad reputation for the cheating agent in second
order reputation systems. But spreading true positive rumors can have very positive
effects for affected agents.
Fast access to accurate reputation information is essential for a prospering system.
This holds especially true in fast changing eco systems like the future Internet of Services.
In order to get as much information from trusted advisors as possible agents may use
the presented inferring algorithm to model their extended network of trust and weight
advisors by trustworthy. This may be a good idea in second order reputation systems
where agents need to find reliable advisors in a shorter time period.
An additional observation is that majority votes seem to enhance the Risk of Herd
Behaviour. A higher threshold than 0.5 might mitigate this threat and may be more
appropriate in many systems. But these ideas are left to future research studies.
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13. References
[1] erep - Social Knowledge for eGovernance. 2009
[2] Avegliano, Priscilla ; Sichman, Jaime S.: Using the RePart Simulator to Analyze
Different Reputation-Based Partnership Formation Strategies within a Marketplace
Scenario. In: al., R. F. (Hrsg.): TRUST 2008. Springer-Verlag Berlin Heidelberg,
2008, S. 226–243
[3] Carrara, Elisabetta ; Hogben, Giles: Reputation-Based Systems: a security
analysis / ENISA (European Network and Information Security Agency). 2007. –
Forschungsbericht
[4] Conte, Rosaria ; Paolucci, Mario ; Sabater-Mir, Jordi: Reputation for Innovat-
ing Social Networks
[5] Golbeck, Jennifer ; Hendler, James: Inferring Reputation on the Semantic Web.
May 2004
[6] Zhang, Jie ; Cohen, Robin ; Larson, Kate: A Trust-Based Incentive Mechanism
for E-Marketplaces. In: al., R. F. (Hrsg.): TRUST 2008. Springer-Verlag Berlin
Heidelberg, 2008, S. 135–161
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