Towards a New Design for Trust Reputation System

Hasnae RAHIMI*

Information Security Research Team (ISeRT): Universite Mohammed V- Souissi, ENSIAS

Rabat, Morocco Hasnae.rahimi@gmail.com

Abstract- Trust is indispensible for any user of an e-service in

order to make a decision before dealing with any transaction.

That's the reason why, users and service providers need various

and functional methods to build on-line trust reputation systems.

This paper discusses the use of trust management and reputation

systems in electronic transactions and particularly in e-commerce

applications. It presents a survey of some existing trust

reputation systems used in e-commerce applications. This survey

proposes a new design for trust reputation systems (TRS) that

focuses on the use of semantic feedbacks in order to calculate

users' recommendation weights and to classify them according to

these weights. This paper highlights the importance of making

the distinction between trustful feedbacks or ratings and

distrustful ones. It proposes also some methods to follow and to

put in practice in order to give the right weight to the right

recommendations.

Keywords-component; Trust; trust management; trustworthiness; decision-making; reputation systems; Recommendation ; semantic feedbacks; rating; e-commerce (key words)

I. INTRODUCTION

E-services have known a great growth in the last ten years. People have no more time to go to real markets, to bargain with sellers, to negotiate or to look for the best opportunity. Once users are in real markets, they do not find neutral advisers who should not rather convince, but just express an opinion based on their historic experience. E-Banking, e-commerce, e-healthcare and e-Iearning are examples of e-services that become indispensable to meet the diverse needs of individuals and organizations. In fact, users of these e-services are often in the obligation to communicate personal information such as their payment card number and are encouraged to share their opinions, feedbacks or recommendations with other users. Users are then exposed to many risks such as not being served at all for what they paid for, to a deception-prone or being victim of deliberately false recommendations. However, in online markets, users face real problems related to trust and reputation. If you want to buy a product, but you don't have enough information concerning it, then you need an advice or a recommendation that is coming from a trustworthy person. Indeed, Trust reputation systems (TRS) are means that can help users discovering the rate of a specific transaction, a score calculated and given to a product or a service or even a user (a seller, a buyer or a referee). But those ratings and

Hanane EL BAKKALI

Information Security Research Team (ISeRT) Universite Mohammed V- Souissi, ENSIAS

Rabat, Morocco bakkali_ h@yahoo.fr

scores do not often tell the truth because the fraud which raises several interests is always present in interactions. Scores and feedbacks may hide bad intentions of certain users who want to falsity the results by giving a positive score when it is a question of promoting their product for instance, or a pejorative score if their interest is a dishonest competition or any other score that has no true nor lived justification or sense, because users can feel a little annoyed so they would like to participate to make kill the boredom. Therefore, we have to eliminate their ratings or to choose the right way to calculate them taking a part of the fraud Severity. But first, those users must be detected among other trustful ones. In fact, feedbacks, opinions and recommendations are full of semantic phrases that could help, within ratings and scores given by the same person, detecting the right intention behind both the rating and the feedback. They actually contain a combination of actionable information that demonstrates a contradiction, an exaggerated negative or positive compromise, simple or neutral concordance an iII­intentioned criticism that is meant more to falsity, to badly compete than to help advising. According to this, how can we affirm that a user is not trustful? Are scores enough to classity a product, a service or even a user? Is semantic expressiveness interesting at classifying users or detecting fraud? Shall we consider feedbacks analysis as a very important, tangible and helpful proof at detecting the fraud? How can we combine ratings and feedbacks analysis in order to prove untrustworthiness? How can we reduce the risk of getting a falsified score? This paper exposes some reputation systems while giving their role. Furthermore, it discusses fraud problems in reputation systems and proposes a solution for reducing risks which prevent the institution of trust. Moreover, we will analyze the problematic below in order to answer the several questions related to trust, user's behaviour through the feedbacks and the scores suggested.

II. BACKGROUND

In order to achieve an electronic transaction such as an e­commerce one, users need to trust the system they are interacting with. In fact, e-commerce applications need to be conforming and following security requirements. It starts from authentication which gives access to most of applications functionalities, until the last transaction which is commonly the electronic payment in e-commerce applications. In each

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function presented by the system, data in input and output needs to be secured (data exchange).

Actually, many systems of trust management are provided to secure applications. They aim at installing and assessing trust in application while providing security requirements. Some of them represent a framework for creating a secure method for exchanging information based on public key cryptography. Besides, reputation systems are trust management systems which aim at creating trust and sharing it in online communities. Users interacting with reputation systems give recommendations, scores and feedbacks concerning services or products in e-commerce applications for instance. This data exchange aim at creating levels of trust in online communities. We can then not only talk about trust assessment but also about trust propagation or transmission. In fact, trust could be shared and transmitted among users while giving their ratings, recommendations and feedbacks. Now, let's define first of all the concept of TRUST.

A. Trust definitions

In the trust management literature, the concept of trust is well studied. In fact, there are many definitions of trust. We will expose some of them that are related to security and e­services. "Where authors mostly agree that it is a rather overloaded concept with many different meanings. In order to foster a meaningful discussion it is therefore useful to define the exact type of trust that is intended when the term is used"[5].

"trust as it is usually conceived is closely related to the willingness to pay, in online markets such as eBay the actual selling price is we will arguery often not an adequate reflection of the underlying trust of the buyer in the seller".[9] Another interesting definition of trust is a defmition given in "Decision Making Based on Trust and Reputation in Ecommerce": "Trust could be perceived as a type of attitude an agent has towards the future evolution of events she depends on, which could be either positive or negative, and also beyond its control [9]. Therefore trust is a subjective evaluation of the potential outcomes and risks involved by relying on a partner. Tightly connected with the notion of trust is that of reputation, which could be seen as a collective, shared assessment of the same aspects."[8]

(Josang et .AI, 2009) use two interpretations of trust: "evaluation trust" meaning the relying party's subjective reliability evaluation of a service object or entity, and "decision trust" meaning the relying party's commitment to depend on a service object or entity.

In this paper, we will define and use Trust as the feeling of

being sure having chosen someone as reference, or something as being the best product to purchase in an online market. Then trust is the subjective sentiment that pushes and incites users to make decisions according to past experiences, to their background or people background or even a common knowledge. It is a measurable since we can answer questions such as how much do we trust in this service, product or person? Then, trust has degrees according to circumstances. Why do we need trust in transactions or electronic services? In

fact, we do need trust in e-services and real or electronic transactions because nothing is insured in markets. We can buy a product which can be deficient, we can pay for a service that we will never benefit from, we can buy a high quality service or product, we will receive it but we will discover that the price was very exaggerated and that we would have bought it from another place with the same quality but cheaper. Consequently, we can build our personal ways and mechanism to install trust and detect several risks in order to make the best decision that would bring satisfaction.

As a result, users do obviously need some automatic means that could store information relative to each product, service or transaction. Thanks to information historic and other's experiences expressed in the web, users can make a decision based on their comprehension to the given information exposed in the reputation system.

B. Reputation systems

"Trust and reputation systems (abbreviated TRS hereafter) represent an important class of decision support tools that can help reduce risk when engaging in transactions and interactions on the Internet. From the individual relying party's viewpoint, a TRS can help reduce the risk associated with any particular interaction. From the service provider's viewpoint, it represents a marketing tool. From the community viewpoint, it represents a mechanism for social moderation and control, as well as a method to improve the quality of online markets and communities". [5] In this paper, we will use this definition since it discusses reputation systems from the service provider's point of view and the community viewpoint. In fact, reputation system are

web application that collect several ratings and feedbacks and use a methodology to calculate and define one rating related to d a product, a service or a user. It is a computer mean that helps users making a decision about dealing with a transaction. Then it's a helpful application for making-decision on purchasing or not a product or a service in e-commerce applications. The reputation created in reputation systems aims at creating and propagating trust among users on several products and transactions. Consequently, reputation systems focus on installing trust by giving ratings and sharing recommendations and feedbacks. Ratings are numeric besides feedbacks are semantic comments that could contain more arguable information. Then feedbacks are more informative than ratings. But leaving a comment takes more time than giving a single rating or selecting a degree of appreciation or depreciation (very sufficient, sufficient, insufficient) for instance. "Reputation is often used in the sense of the community's

general reliability evaluation of a service entity" [5]. However, in our paper we will combine recommendation (semantic feedback) and ratings if they exist in order to calculate a trust weight for a single user. Both of them are important since semantic feedback contains justifications and arguments in some cases that could help convincing users to deal or not deal with a specific transaction. In fact, the study made in "Online reputation: Measuring the effect of semantic feedback on trust and trust repair" confirms the importance of the textual feedback: a single negative comment has a strong effect on the

attractiveness of the offer. It is also possible to counteract the negative effect of textual feedback by choosing the right strategy in constructing the reply. [9] However reputation systems depend on the domain of their application. "For example, providing financial advice and providing medical advice represent two different scopes for which trust and reputation should be considered separately. Trust and reputation also exist within a context. The context refers to the set of underlying characteristics of the domain in which a TRS is being applied. For example, there can be policies and regulations, and degrees of enforcement of those regulations. The context can influence risk attitudes and how trust and reputation scores are interpreted in practical situations". [5]

Besides, users could be classified and measured according to the reliability and trustworthiness of their feedbacks, scores or recommendations. Each user then is represented by a weight relative to his trustworthiness. This is the approach analyzed and discussed in our model.

B.] Weight of recommendation

"In relation to trust systems the term "recommendation" is often used in the sense of a trust measure passed between entities, whereas the term "rating" is often used with relation to reputation systems. In this paper we will use the term "rating" to denote both. A "score" will refer to a measure of trust or reputation derived by a TRS function based on the received ratings." (Josang et.AI, 2009)[5] There are many methods to calculate recommendation weight or ratings. For instance, "the final result depends on the ratio between the number of favourable ratings and of total number

of ratings". [8] We can give our definition to recommendation weight as being the rating that reflects the trustworthiness of a recommendation, a feedback or even a scoring suggested and decided by a user. In fact, he or she can leave a comment that possesses a total or partial or none part of truth and then trustworthiness. Then, we do need a tangible variable that reflect, describes and translates a score or feedbacks reliability. Several methods calculate the weight of trust and reliability.

However semantic sentences, opinions and recommendations given by users are more expressive than scores (rating) because they contain arguments, justification and emotions.

B.2 Semantic Feedback

Our paper aims at analyzing the textual feedbacks in order to calculate the right weight of recommendation because "the content of textual feedback can strongly influence the reputation of online traders. The magnitude of a single text comment can be large; much larger than the effect of the bare reputation score."[9] In fact, we can define semantic feedback as a comment, information shared by a user in order to give recommendations, references, advises or an expression for an opinion which could be pejorative or positive. In almost cases, they are more subjective opinions than objectives. Actually, while booking in the net, users are always looking for advisers and referees

because they can contribute in users' decision-making. However, those contributors are able to influence them in the positive or the negative way. In fact, users looking for recommendations need true opinions and faithful one more to convince them and let them analyse properly their situation then to influence them without right arguments and logical justifications. Consequently, trust is indispensible to make a decision before dealing with any transaction. That's the reason why, we do really need various and functional methods to build on-line trust reputation systems.

III. RELATED WORK

Actually, trust and risk are important in e-commerce for consumers to persuade them making the right decision of dealing with a transaction. Researchers explain that trust and risk of untrustworthiness influence the consumer's decision to purchase on line [I]. They develop a theoretical structure describing the relation between decision-making and trust which handles a consumer making an on-line purchase. Their study tests the proposed model by using a Structural Equation on the Web consumer referring to purchasing behaviour collected via a Web survey. The results of the study show that the risk felt by Internet consumers has strong impacts on their purchase decision. In fact, reputation systems are essential since they help users to trust in the product, the service provided or in dealing with a specific transaction. Reputation systems are applied in various fields but they all aim at distinguishing between the services provided and classitying them according to users' interventions. As a matter of fact, reputation systems depend on the domain of their

application as the paper" TRIMS, has privacy-aware trust and reputation model for identity management systems" confirms [2]. This paper stresses fields of application of the TRS while giving trust definitions. It also supplies the objectives behind trust and reputation systems. The paper aims at presenting a method that can be used in reputation systems and which tends to weight users' recommendation. In fact, we will use this method in our model but we will add a redirection to another interface in order to give users other feedbacks to support or not (like/dislike). We used this method because its intent is to calculate a more trustful score and it consists in calculating a weight for every user recommendation by using the digital certificates. We are going to use additional methods to calculate both user trustworthiness weight and the product commented score.

Besides, one of the greatest advantages offered by internet is that it largely reduces the transaction costs of collecting,

processing and distributing information [3]. Besides, On-line Reputation systems (ORS) guarantee an acceptable level of security when deciding to consider sensitive user's contribution as reliable or less. In fact, (Ling Liu et al. 2010)[3] explain that ORS collect users' opinions on products, transactions and events as reputation information then aggregate and publish the information to the public. Their study proposes an evaluation mechanism which aims at measuring different reputation systems in the same context and

covering all aspects of the systems. The authors established a comparative study of the various systems of reputation of the biggest web sites that are the most visited and where we supply most possible opinions in different ways and subjects (eBay, Amazon, Digg). In EBay as in Amazon a system of electronic payment is essential to finish the electronic transactions. (Changsu Kim et al. 2009) [4] examines issues considered by the customer and related to e-payment security. Their study proposes a conceptual model that outlines the determinants of consumers' perceived security and perceived trust and their effects on the use of e-payment systems. Securing the transport of important data such as the number of the banl<- card intended for an electronic transaction is a guarantee of the success of the

whole electronic transaction and its completion. In order to set robustness requirements in a trust reputation system, it is indispensable to know how important robustness really is in a particular online community or market. (Audun Josang and Jennifer Golbeck, 2009) [5] explains the objectives behind trust and reputation systems. The authors discuss the research of challenges for trust and reputation systems, and propose a survey research made on 3 TRS which are Slashdot TRS, Advogato TRS, and kuro5hin TRS. Their paper develops reliable robustness principles and mechanisms designed for trust and reputation systems. It also focuses on the importance of the intention behind TRS manipulation in an e-commerce application. The paper gives definitions to manipulation's types such as "collusion", "proliferation", "Reputation Lag Exploitation" etc. Some of those TRS manipulations aim at providing unfair recommendations or discrimination. We are not going to use the methods used in the TRS below because

most of them need human intervention which is not reliable.

"Trust Management in Online Communities" [6] supplies various semantic aspects of trust with various methods to build on-line a trust reputation system TRS. This paper also explains problems and challenges to build and implement a reliable and strong TRS. Audun JOSANG in this paper presents also potential solutions to resolve problems faced in TRS. However in this paper, there is evocation of two reliable examples; one is real however the other one is fictitious. In fact we do have some critics: in these 2 examples, there is a mention of the need: when the need is intense and real, we tolerate the risks but when the need is not too much expressed, we take into account the slightest risks. JOSANG evokes in the same paper the natural transitivity of trust as being a collection of opinions from people on whom we rely. However he gives an example that cannot be useful in online communities. In fact, in his example "Eric" showed to "Bob" that he is reliable but only in a single case or several cases but not in any case. Then "Eric"

is not always trustful or reliable since we do not demonstrate a theory by proving its validity in one single case. Trust depends on the user and prior transactions dealt with the user and recommendations given by this user. Moreover, physical commerce is very different from electronic one because traditional communities rely on physical communication whereas online communities rely on TRS where users can create and propagate trust. Our model takes in consideration all those critics discussed below.

(Josang et. AI, 2011) [7] explains the role that personal plays in order to produce the correct interpretation according to trust recommendations and conclusions, in order to make the right decision while dealing with e-transaction. This paper aims at presenting the relationship between taste and trust in the analysis of semantic trust networks. In fact, an opinion relies on person's ability to give a judgment on a product. It relies also on the precedents transactions made by the person whether he or she used the product, the service or not and especially whether the user is reliable and trustworthy. Actually, some firms engage people who become in charge of giving positive opinions on their products or services. They do not consider such act in any way as being illegal act but as marketing

strategy that boosts the firm and in the other hand it falsifies trust reputation systems. What is new about our model is that it calculates the user trust weight and the score of the product not only using scores given by users but also using semantic feedbacks in a specific combination. ( see Our Model) In fact, (Chris Snijders et.AI, 2011) [9] explain that many studies have examined the influence of the numerical reputation indicators in feedback profiles. In fact, this influence is reflected by the probability of sales and whether higher reputation scores encourages selling even if the price is higher. Furthermore, the authors affirm that focusing on the text comments (feedbacks and textual recommendations) in addition to the numeric feedback is important for at least three reasons. First, research suggests that the majority of for instance eBay users read at least one page of text feedback comments about a seller. Apparently, the text matters. Second, given that users indeed consider the text comments, not much

is known about the way in which the text comments affect trust

between buyers and suppliers. Third, it is not clear how the size of the effect of text comments compares to the size of the effect of reputation scores [8]. In fact, this research was beneficial since it demonstrates that score is not enough to estimate, underestimate a product or service or a specific transaction. The application created in this study allows creating online experiments using the Choice-Based Conjoint (CBC) analysis paradigm. The interfaces resemble real eBay user interfaces. They develop an experimental design in which they asked 191 current of former eBay users in order to compare the attractiveness of some product offers. They varied the characteristics of sellers in each offer, including their reputation score, existence and type of semantic textual comments and rebuttal comments. After these comparisons, users were asked to evaluate the trustworthiness of the manipulations used in the experiment especially if there were different types of trust violations and

seller's reactions. This is a very interesting study because it demonstrates by an experiment the importance of textual and semantic feedbacks in making purchase decision in e-commerce. In fact, our methods to calculate recommendation weight needs semantic feedbacks more than the score and if we do have both we will use both in order to determine the right weight for the right recommendation. "Decision Making Based on Trust and Reputation in Ecommerce" [8] is a paper written by loan

Alfred Letia and Radu Razvan Slavescu in 2011. They use a logical model in order to build decision making mechanism based on trust and reputation system. This built mechanism is used then for simulation in an interactive community such as a forum or reputation interface in which users can publish exchange and share their opmlOns and reputation recommendations. The paper gives a new definition in order to classify, select and integrate the obtained information according to its trustworthiness. In this paper, there are two approaches of ratio calculation. One is centralized and the second approach is a distributed one since the agents do not put their feedbacks or ratings in the same interface of the reputation application but there are transmission and distribution of opinions among agents. This approach is close to our approach adopted in our paper since we're going to redirect users to other interface that contains other user's feedbacks and fabricated ones. Then the user can be able to like or dislike the proposed feedbacks.

IV. OUR MODEL

This paper proposes a new design for trust reputation systems TRS that aims at giving methods to calculate scores related to product or services in e-commerce communities. This new design focuses also on the use of semantic feedbacks in order to calculate a recommendation weight and to classify users according to their recommendation weight.

A. Introducing Semantic feedback in the TRS design

The robustness of trust reputation systems TRS implies the competition reliability and the trustworthiness of the reputation score. Once those constraints are available, we can talk already about robust TRS. TRSs are functionally integrated with the interaction partners in an online community or market. A service entity is assumed to faithfully provide a service to the relying party. A relying party is assumed to rely on scores for its service selection function, and to faithfully provide ratings about service entities or about specific services to the TRS function. For example, a relying party that colludes or that is identical to the service entity could provide fake or unfair positive ratings to the TRS with the purpose of inflating the service entity's score, which in turn would increase the probability of that service entity being selected by other relying parties. Consequently, bad TRS manipulation behaviours engender neither unfaithful nor trustful reputation ratings. In fact, any firm selling on the net its product or exposing its service to sell can install a TRS application in order to distinguish between trustful and untruthful ratings and feedbacks. In this paper, we propose some principles to be followed in a TRS design. In fact, the client must have brought a product or a service and dealt with the electronic transaction proposed by the firm application integrating of course the TRS application we are defining. An authentication can prove its transaction. In the majority of such application, users must authenticate in order to purchase online. In other words, all information about users is stored in the database application since users are becoming client (cookies and sessions). When the user is willing to rate or comment a service or a product, he or she must present, as a proof of being a relying party, a serial

number or scan a bar-code of the done purchase which must be unique as a primary key of the done purchase. For instance, laptops, cell phones or any other product or service that must be bar-coded or possessing a serial number. TRS can involve human interventions but less they imply human interventions more TRS are robust and truthful. In fact, after the user gives such reliable information, an algorithm verify, basing on the database containing all information concerning historic purchases, whether the code or information shared is correct or not. Users can insert information in a short data form proposed by the TRS application, in order to simplify the data extraction and the interaction. Users can either give rating and feedback. Both must be accompanied with binary information (image bar­code) or serial number or the transaction number which must be unique for each user (buyer). This method is to be followed when the application has the database of all information about sells, in other case we cannot make the verification explained below. There are three types of rating a product, service or a transaction, either it is a score (on scale of 5 for example), or personal opinion (feedback) or by selecting a given appreciation or depreciation (like/dislike). A distributed approach can be adopted when agent are going to give their personal feedbacks. We will give each user the right to comment on other user's textual comments. In fact, we're going to redirect users in other interface (web page) that will expose some selected user's comments related and associated to the criteria of his or her previous textual comment. Not only other user's comments but also some fabricated comments that we created. Some of them can be true or half true or false or very unfair. Each of them will have a specific rating according to its trustworthiness. In fact, we will give positive and negative comments according to the content of the comment of the user or the product scored by the user. Consequently for each product we have to give a selection of feedbacks. Of course, an algorithm will be in charge of such approach. We can propose a methodology to classify these feedbacks in categories. In fact, in the database we're able to list positive, negative, neutral or mitigate words (key words) and terms concerning any product. Then the algorithm can contain automatic "select" queries (triggers for instance, stored procedure or combination) on the user's feedback which can be a Varchar variable for instance. Then we can calculate the proportion of the positive, negative, neutral and mitigate words. The greatest proportion will give the information on the category of the feedback. After that, we can classify the feedback and redirect the user into the web page containing the

selected comments (stored in the DB) related to the category of his or her comment on a specific product. In this situation, we have the trustor and trustee. The relation between them resembles the relation between the grantor and the grantee in database administration. In fact, the option «with grant option» given with a permission to a user allows him or her to give on his turn the same received privilege. We will use this analogy in the meaning that a user is allowed to use other user's comment if he has a great trust weight. Of course, the

comment that will be used as a recommendation for instance must have a specific score. We will then create a community

and a circle of allowed and authorized users which is a circle of trust. To be authorized to use a comment is not permanent but we can specify some types of interventions on the system

where re-verifying is necessary. We can use the notion "critical mass" [8] in order to judge that a comment is trustful or deserves its score; most users must corroborate and agree with

the level of trustworthiness and reliability estimated for it.

B. Introducing trust weight

In fact, while redirecting a user in the other web page containing fictive and real comments, every comment will have

a score depending on its trustworthiness. The user will have a

chance to select the comments they like or not. In that way, we have tried to influence the user point of view. If he changes his previous point of view set in the first comment, he lies, or has bad intentions then we can detect it. However in "TRIMS, a privacy-aware trust and reputation model for identity

management systems" uses a method in order to calculate the trust weight. "Once the transaction has been carried out between the Web Service Providers WSP and the Web Service Consumers WSC, a reward or punishment is distributed to users and WSPs according to the accuracy and reliability of

their recommendations". They focus on the punishment and reward of users (it is equal for WSPs). So WSP 1 sends to IdP the satisfaction of the user who asked for the service, together with a certain threshold d.

A simple mechanism will be established to measure the divergence between the final satisfaction of the user, Sat, and the previously given recommendation of users Reci.

In fact, in our model we are not going to use other users' recommendations to determine the trustworthiness of the current user since even those recommendations can be

untrustworthy and this is the difference between our method and the method used in [4]. In this paper, we will establish a method in which we will use

the value of Reel which is the previously given recommendation of the current user for whom we're going to determine the recommendation weight and Rec2 which is the

recommendation given by the same user in the web page containing feedback (after redirection). We will use [X,Y] which is an interval of scores to be determined in function of

the category of feedbacks mentioned in the web page. Therefore, if Rec2 € [X,Y] then a reward is performed over user and according to the value of Rec2: if it is close to an

optimal value 0 € [X,Y] we will affect t*Reel to the product as a rating (trustful rating) with "t" reflects the closer is Rec2 to

the optimum O. The reward is that we will classify the user as

being trustful and according to his or her recommendation weight established. Rec I can be calculated as the score S given by the user previously in addition to a score F given to a feedback if he or she gives it. Then Recl= S+F.

Otherwise, if Rec2 (J. [X,Y], then user is punished and we will

classify this user in the untrustworthy circle for that product. Both punishment and reward are proportional to the distance between the recommendation given before the redirection by the user and the recommendation given after the redirection.

We can calculate Rec2 by using the method of [4] while

considering other users' recommendations as the other

comments on the web page after redirection. We can propose another way to calculate Rec2 by summing the studied ratings (negative and positive) of each feedback in the web page. If we

have S comments we can have RI+R2+R3+R4+RS where Ri is the rating associated to the feedback i and it could be negative

or positive according to its trustworthiness and to the choice of the user (like or dislike).

v. CONCLUSION

Lack of trust is always considered as an obstacle that does not

help user dealing with electronic transaction. Trust reputation

systems aim at creating trust and propagating it in online

communities while giving actionable results. Those results such as trust weight and scores help users making a decision about purchasing or not a product from an e-commerce

application. However those scores are not always truthful. Then, they can falsify the weight and the ratings. Semantic

feedbacks are more meaningful than single scores. This paper proposes a design to be completed and verified by an experiment. This design will use both ratings and especially semantic feedbacks in order to calculate trust weight and to classify comments and users. This approach is based on an

algorithm that analyses semantically users' feedbacks. As a perspective, we will develop give more details about the parameters discussed below and the method to calculate them. We will develop the algorithm in order to experimentally

simulate the trust reputation system.

REFERENCES

[I] "A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents" (Dan J. Kim, Donald L. Ferrin, H. Raghav Rao) published in Journal Decision Support Systems. Volume 44 Issue 2, January, 2008 Elsevier Science Publishers B. V. Amsterdam, The Netherlands.

[2] TRIMS, a privacy-aware trust and reputation model for identity management systems ( Felix G6mez Marmol , Joao Girao , Gregorio Martinez Perez) Elsevier sept 6th 2010.

[3] Evaluation Of Collecting Reviews in Centralized Online Reputation Systems (Ling Liu, Malcolm Munro, William Song): 6th International Conference on Web Information Systems and Technologies 2010.

[4] "An empirical study of customers' perceptions of security and trust in e­payment systems" (Changsu Kim, Wang Tao , Namchul Shin, Ki-Soo Kim), Electronic Commerce Research and Applications/ Science direct! ELsiever 2009.

[5] Challenges for robust trust and reputation systems (Audun JOSANG, Jennifer GOLBECK) 5TH international workshop on security and trust management 2009.

[6] Trust Management in Online Communities (Audun JOSANG), Workshop on New Forms of Collaborative Production and Innovation: Economic, Social, Legal and Technical Characteristics and Conditions. SOFI, University of Giittingen, 5-7 May 2010.

[7] TASTE AND TRUST (Au dun Josang, Walter Quattrociocchi, and Dino Karabeg), 5th IFIP Trust Management Conference (IFIPTM 2011), Copenhagen, June/July 2011.

[8] Decision Making on Trust and Reputation in e-ommerce (loan Alfred Letia and Radu Razvan Siavescu) 15/09/2011 ICORE 2011 international conference on Reputation, France.

[9] Online reputation: Measuring the eflect of semantic feedback on trust and trust repair (Chris Snijders, Uwe Matzat, and Marcin Bober) 19/09/2011 ICORE 2011 international conference on Reputation, France.