publish able quality on web reputation systems

8/2/2019 Publish Able Quality on Web Reputation Systems

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Afshan Saleem,

Sameen Javed,

Fakhira Nazir

MSCS - 18

4/23/2012

Technical Report of Publishable Quality on Web

Reputation Systems


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Table of Contents1. Introduction ............................................................................................................................. 4

2. Literature review ...................................................................................................................... 5

3. Publishable Quality on Web reputation System ...................................................................... 7

3.1. Reputation concept: .......................................................................................................... 7

3.1.1. Characteristics of Reputation: ................................................................................... 7

3.2. Trust and Online Trust Concept: ...................................................................................... 8

3.3. Web Reputation System: .................................................................................................. 8

3.3.1. Negative and Positive Reputation ......................................................................... 9

3.3.2. Features of Web Reputation System ..................................................................... 9

3.3.3. Problems of Web reputation System ................................................................... 13

4. Existing reputation systems: .................................................................................................. 15

4.1. eBay: People are basically good.......................................................................... 15

4.2. Amazon: .............................................................................................................. 17

4.3. Epinions: A web of trust...................................................................................... 20

4.4. Face Book:........................................................................................................... 20

4.5. You tube: ............................................................................................................. 20

4.5.1. Issues [11]: ..................................................................................................... 214.5.2. Key Recommendations [11]: ......................................................................... 21

4.6. Email: .................................................................................................................. 21

4.7. Yahoo Answers: .................................................................................................. 22

5. Conclusion ............................................................................................................................. 24

6. References ............................................................................................................................. 25


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List of Figures:Figure 1. Online Reputation ......................................................................................................... 7

Figure 2. Features of reputation system ..................................................................................... 10

Figure 3. Comments ................................................................................................................... 10

Figure 4. Facebook Tags ............................................................................................................ 11

Figure 5. Rankings ..................................................................................................................... 12

Figure 6. eBay feedback stars .................................................................................................... 12

Figure 7. Reviews ...................................................................................................................... 13

Figure 8. Simple Rating ............................................................................................................. 13

Figure 9. Some existing Reputation Systems............................................................................. 15

Figure 10. eBay Reputation System ......................................................................................... 16

Figure 11. eBay Feedback page ............................................................................................... 17

Figure 12. The Amazon Reputation System for Sellers ........................................................... 18

Figure 13. The Amazon Reputation System for Products ........................................................ 18

Figure 14. You Tube Reputation System ................................................................................. 20Figure 15. You tube current design and re-design ................................................................... 21

Figure 16. Yahoo answers Reputation system ......................................................................... 23

List of Tables:Table 1. Comparison of Amazon Pre-Web 2.0 and Web 2.0 Reputation systems ................... 19


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1.IntroductionAs the Web becomes increasingly distributed with content being created on the edge, and largenumbers of individuals and organizations use Internet media to research/exchange information,and to conduct business transactions, the need for establishing trust mechanisms within online

communities in order to facilitate these activities becomes apparent [1].

An online reputation system is the primary mechanism used by online markets to collect,distribute, and aggregate feedback about participants past behavior and help people to decide

whom to trust, and to encourage trustworthy behavior. It is argued that in order to effectivelyfoster trust among strangers, it is important to track historic data, and establish the shadow ofthe future in an online environment [1].

Among the existing reputation systems, ebays feedback forum is one of the most studied. ebays

system allows buyer and seller to rate each other and leave comments after each transaction, thecumulative feedback score is then visibly displayed along each users screen name. Empirical

evidence indicates that sellers with better reputations are more likely to sell their items on ebay[1]. In fact, the overall commercial success of eBay is largely attributed to the design of itsreputation system.

Online reputation systems are currently receiving increased attention while online interactionsare flourishing. However, they lack one important feature: Globality. Users are allowed to builda reputation within one online community, and sometimes several reputations within severalindependent online communities, but each reputation is only valid within the correspondingcommunity. Moreover, such reputation is usually aggregated by the provider of the onlinereputation system, giving the querying agent no say in the process.


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2.Literature reviewIn paper [1], Author provided online reputation system in web era 2.0. Web 2.0 has transformedhow reputation systems are designed and used by the Web [1]. Based on a thorough review ofthe existing online reputation systems and their challenges in use, this paper [1] studied a case of

Amazons reputation system for the impacts of Web 2.0.Through case study, severaldistinguished features of new generation reputation systems are noted including multimediafeedbacks, reviewer centered, folksonomy (use of tag), community contribution, comprehensivereputation, dynamic and interactive system etc. [1]. These new developments promise apath that move towards a trustworthy and reliable online reputation system in the Web 2.0 era[1].

David Parkes and Seuken [3] discussed reputation systems as an online mechanism thataggregate feedback from users past exp eriences, to enable more informed decisions of other

users in the future. They [3] explained the different function of reputation systems to solve themoral hazard problem as well as the adverse selection problem. Also discussed several important

design levers of reputation systems, and taken a close look at the design of the eBay reputationsystem, before and after an important design change[3].

In the paper [5], taxonomy of Email Reputation Systems, Authors examined the requiredproperties of email reputation systems, identified the range of approaches, and surveys previouswork [5]. As there has been previous work in the area of email reputation systems that canaccomplish these broader goals by collecting, analyzing, and distributing email entities'past behavior characteristics [5]. The goal of an email reputation system is to monitor activityand assign a reputation to an entity based on its past behavior. The reputation value should beable to denote different levels of trustworthiness on the spectrum from good to bad [5].Now that several reputation systems are emerging there is a need for a single framework that

allows them to be plugged in and consulted easily. The framework presented here provides anoutline towards how the different types of existing reputations systems can be integrated[5].

Audun, Ismail, and Colin [12] had done a survey of Trust and Reputation Systems for OnlineService Provision in the paper [12]. Trust and reputation systems represent a significant trend indecision support for Internet mediated service provision. The basic idea in the paper [12] is to letparties rate each other, for example after the completion of a transaction, and use the aggregatedratings about a given party to derive a trust or reputation score, which can assist other parties indeciding whether or not to transact with that party in the future. Reputation systems can be calledcollaborative sanctioning systems to reflect their collaborative nature, and are related to

collaborative filtering systems. Reputation systems are already being used in successfulcommercial online applications [12]. There is also a rapidly growing literature around trust andreputation systems, but unfortunately this activity is not very coherent. The purpose of this article[12] is to give an overview of existing and proposed systems that can be used to derive measuresof trust and reputation for Internet transac-tions, to analyse the current trends and developmentsin this area, and to propose a research agenda for trust and reputation systems [12].


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Audun [13] presented online reputation system for the health sector [13]. The article [13]describes robust principles for implementing online reputation systems in the health sector. Inorder to prevent uncontrolled ratings, our method ensures that only genuine consumers of aspecific service can rate that service [13]. The advantage of using online reputation systemsin the health sector is that it can assist consumers when deciding which health services

to use, and that it gives an incentive for high quality health services among health serviceproviders [13].

In the paper [14] Milad Sharif and Soheil Norouzi had provided Sentiment Based Model forReputation Systems in Amazon. The data set includes details of the 9,500 transactions thattook place on Amazon.com for 280 different software products [14]. The data set collectedfrom publicly available information at Amazon.com by using Amazon Web Services over aperiod of 180 days, between October 2004 and March 2005 [14]. Authors [14] used differentbinary classification models to accurately predict the polarity of the premium price that amerchant get based on the costumer reviews. Evaluation shows that RAE can predict thesedistributions more accurately than other models [14].


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3.Publishable Quality on Web reputation System3.1. Reputation concept:

One of the many ways to foster trust in online interactions is through collecting and managinginformation about interacting parties past behavior [7]. This information can then be aggregated

in order to come up with a summary evaluation, which is called reputation. Reputation is a

variable which depends on feedback about an interacting partys past behavior given by others

and it affects the interacting partys future payoffs [7].

Reputation is basically general opinion or judgment of people towards an entity. It is aubiquitous, spontaneous and highly efficient mechanism of social control. It is a multidisciplinary phenomena as described in the below figure.

Figure 1. Online Reputation

3.1.1. Characteristics of Reputation:Reputation information is generalized by combining personal opinions and opinions from othersfor the same reputation subject [7]. According to [7] information sources for reputation can beclassified as primary sources or secondary sources. Primary reputation is obtained from directinteractions or observations of those interactions. Secondary reputation is obtained from othersopinions [7]. These reputations can be summarized as several different types of measurements,such as a number, a percentage, a word or an expression. The type of measurement is chosen

according to the different levels of detail about the reputation. For example, good or bad canonly describe reputation approximately.

Four distinct types of agents are involved when evaluating the reputation of an agent [7]. Theyare:

Evaluators: these are agents who can develop an evaluation or evaluative belief aboutother agents, including individuals, groups, organizations, etc [7]. The information usedby evaluators can be direct experiences with the targets or through third parties.


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Targets: these are agents that play the role of the evaluation object [7]. Beneficiaries: these are individuals, groups, organizations, etc. who benefit from the

evaluation [7]. Propagators: these are third parties that can propagate the reputation information to

other agents who need the information, usually beneficiaries [7]. In order to propagate the

reputation information, a functional ontology of reputation needs to be defined.

3.2. Trust and Online Trust Concept:Trust is defined as a relationship of reliance [7]. Trust can be treated as a social phenomenonor a complex notion [7]. Moreover, trust is the perception of the degree to which an exchange

partner will fulfill their transactional obligations in situations characterized by risk oruncertainty [7]. In other words, trust provides a certain degree of security before taking actionwith transaction partners despite incomplete information and uncertainty.

Trust is often discussed in the online context [7]. Online trust shares similar characteristics with

those of offline trust. However, there are some important distinctions in an online environment.

To facilitate a better understanding of the nature of trust in an online context, four characteristicsof trust are listed below [7]:

Trustor and Trustee:in any trusting relationship, there must be a trusting party (trustor) thattries to trust another party (trustee). These two parties can be individuals or organizations. Inonline situations, a trustor may be a user who is browsing an e-commerce web site and thetrustee in this case may be the e-commerce website itself. In reality, the trustee is the merchantthat the website represents [7]. A trustee can also be another user who wants to sell a productonline (i.e., on eBay). Trust is based on the evaluation about the ability of the trustee to satisfythe needs of the trustor and the degree of trust that the trustor places on the trustee [7].

Vulnerability: trust is only needed in an environment that is uncertain and risky. In onlinesituations, merchants or individuals may behave in an unpredictable manner because of the highcomplexity and anonymity associated with e-commerce transactions. Consumers are vulnerable[7]. They may be cheated and their financial information could be stolen when they aretransacting online [7].

Produced actions: trust leads to actions [7]. For instance, you lend money to your friendsince you trust that s/he will return it to you later. Consumers would provide credit card andpersonal information during a purchase if they trust online merchants [7].

Subjective matter: trust is not an objective matter but a subjective degree of belief abouttrustees [7]. It is affected by individual differences and situational factors. Different people couldhave different degrees of trust towards different trustees in different scenarios. For example, two

best friends would have different past experiences with the same product, so one of them truststhis product and another does not [7].

3.3. Web Reputation System:Online reputation systems are community tools that collect, distribute, and aggregate feedbackabout participants past behavior [7]. They help people decide whom to trust, encourage

trustworthy behavior, and discourage unskilled or dishonest participations [7]. Reputation


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systems are related to collaborative filtering systems. Reputation is that it is oftencharacterized as context-specific, multifaceted and Dynamic [1]. In a web environment, onlinereputation systems are important mechanisms for identifying the credits of products, individuals,and organizations [1]. Nowadays, user feedback is an important part of many activities on theInternet [3]. After a transaction on eBay, buyers rate sellers and sellers rate buyers; after a

transaction on Amazon, buyers rate products and write a review, and after watching or buying amovie online, viewers rate the movie, and so forth. Moreover, there is strong evidence thatconsumers take the aggregated information from these sources of feedback into account whenmaking purchase decisions [3]. And without the feedback from other users, we would often havea hard time finding a good hotel for the next vacation, determining who is a trustworthy seller oneBay, or what is a good restaurant in the local neighborhood? If a user cannot inspect a productbefore she buys it, she must rely on others feedback regarding the quality of the product. If a userdoesnt know that a seller has had a store for many years, she must rely on previous customers

feedback regarding the trustworthiness of the seller [3].

Computer-based reputation systems based on cumulative scoring of participants experiences are

economic and non-economic on the internet and used for a wide range of purposes [8].Reputation Systems rate ecommerce, products, services, teachers, nightspots, companies,bloggers and much more. For examples [8], visit sites like epinions (http://www.epinions.com/ ),which uses up to five stars (1 low to 5 high); Amazon.com (up to five stars); Bizrate(http://www.bizrate.com) green smiley or red frown faces; virtual ratings(http://www.virtualratings.com), A (high) to D (low) for professors or RateMyTeachers (http://us.ratemyteachers.com/); Citysearch (http://www.citysearch.com) for numeric ratings onrestaurants and nightspots; the Internet Movie Database (http://www.imdb.com/), which uses 10stars and Top Radio weblogs (http://radio.weblogs.com), which uses the number of peoplesubscribing to feeds [8]. Economic reputation systems distinguish between trustworthy and ontrustworthy sellers and have the added impact of pressuring sellers to be trustworthy whilediscouraging those who arent [8].

3.3.1. Negative and Positive ReputationPositive Reputations represent the relative value of an entity or user. Sometimes known asrelevance,popularity, or even quality, positive reputation is used to feature the best content andits creators. Negative Reputations identify undesirable content and users for further action. Thisincludes illegal content, TOS violations, and especially spam.

3.3.2. Features of Web Reputation SystemSome of the feautres of web reputation systems are mentioned in the below figure.


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Figure 2. Features of reputation system

3.3.2.1. Comments:Through comments reputation or overall ranking of the page or product can be guessed. Each

user present his own perspective about the services he is using. Through commenting user is able

to add new story on the web or add some additional facts about the given story.

Figure 3. Comments

3.3.2.2. Questions & Answers:The websites that are dedicated for the service of people allow them to interact in the form of

question and answer sessions. We have got many platforms for this purpose like wiki answers,

yahoo answers, answerbag, yedda and many others. Wiki answers allow you to select multiple


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categories. It has Nice and simple layout with friendly community. On the other hand it has no

additional details section and is impersonal whereas Yahoo! answers have an additional details

section with easy and simple to use.

3.3.2.3. Tags:Facebook recently announced an enhancement to their core tagging system that allows Facebook

photos to be tagged with company pages. Assuming your blog has a Facebook presence this

could mean a number of things for both your blog, and Facebook. This feature was rolled out

recently, and incorporates the ability to tag your blogs Facebook page on peoples pictures.

Effectively minimizing the gap between product pages and people pages; and in some regards

business pages are now more powerful than personal pages.

Figure 4. Facebook Tags

3.3.2.4. Rankings:The Internet is composed of billions of pages of hypertext, put up by corporations, organizations,

and individuals. Because it is open and anonymous, anyone can post information on a web site.

However, judging the authenticity of a source is a crucial part of what search engines likeGoogle need to do to generate useful, relevant results. The PageRank algorithm [15], developed

by Googles founders, is one of the reputation systems examined later in this thesis: it can be

viewed as a reputation system which models the web as a graph, where each web page is a node

and directed edges represent hyperlinks between pages. When particular queries are searched

through Googles search engine, the first results shown are those with the best PageRank scores,

appropriately weighted by some measure of the relevancy of the page to the search term. This


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explosion of information availability has also made us more dependent on search engines like

Google for finding and organizing information. These search engines in turn drive the

development of reputation systems like Googles PageRank algorithm, which rate the reliability

of web sites by examining the hyperlink structure of the web (sites which are linked to more

frequently should be thought of as more authoritative). For web site owners, the relative ranking

or reputation of a web site can cause huge shifts in the amount of incoming traffic and

advertising revenue. Yet the reputation systems underlying search engines are rarely transparent

and available: Google relies heavily on secrecy to prevent web site owners from optimizing their

sites for higher rankings. For businesses dependent on income from Google-driven traffic, this

lack of transparency is unsettling to say the least. This has spurred work on incentive-compatible

reputation systems which cannot be manipulated. The rules for such systems can be published

openly, addressing this need for transparency.

Figure 5. Rankings

3.3.2.5. Feedback:Each time you buy or sell something, you have an opportunity to leave Feedback about your

experience. Feedback consists of a positive, negative, or neutral rating, along with a short

comment. Buyers and sellers build reputations that are based on all the Feedback ratings and

comments left by their trading partners.

Figure 6. eBay feedback stars


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3.3.2.6. Reviews:Some ratings are most effective when they travel together. More complex reputable entities

frequently require more nuanced reputation models, and the ratings-and-review model, allows

users to express a variety of reactions to a target. While each rated facet could be stored and

evaluated as its own specificreputation, semantically that wouldn't make much sense-it's the

review in its entirety that is the primary unitof interest. In the reviews model, a user gives a target

a series of ratings and provides one or more freeform text opinions. Each individual facet of a

review feeds into a community average.

Figure 7. Reviews

3.3.2.7. Ratings:When an application offers users the ability to express an explicit opinion about the

quality of something, it typically employs a ratings model. There are a number of different

scalar-value ratings: stars, bars, HotOrNot, or a 10-point scale. In the ratings model, ratings are

gathered from multiple individual users and rolled up as a community average score for that

target.

Figure 8. Simple Rating

3.3.3. Problems of Web reputation SystemHui Li [7] identifies the following six major problems with current online reputation systems:

(1) Inaccurate equations: some existing reputation management systems use equations whichcannot accurately reflect the ratees reputation. For example, eBay uses simple summation to

calculate reputation scores, and so does Yahoo! Auction and Auction Universe. On eBay, a userwho has had 100 positive ratings will have the same reputation score as a person who has had300 positive and 200 negative ones.


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(2)Barrier to entry: users usually start with a reputation of zero or a very low reputation score.This would be a barrier for them to enter into the market, since many users would not deal withthe ones with low reputation scores.

(3)No Incentive to rate: there is no incentive for users to rate transactions. This would lead to

insufficient ratings hence an accurate reputation score.

(4) Inability to filter or search: some reputation systems face information overload problems.EBay does provide the ability to search for a particular item, but it does not include reputation asa search criterion. For instance, an eBay user might search for a Canon digital camera from aseller who has a reputation score above 50. The ability of filter and search by reputation woulddefinitely improve the efficiency and usability of reputation systems.

(5) Categorization: until recently reputation has been considered one-dimensional in mostreputation systems. Actually, entities may have many reputations. As mentioned earlier, forexample, a person would have a high reputation on product quality, while having a low

reputation delivering on time.

(6) Unlimited memory: many reputation management systems have unlimited memories. In thatcase, they would use all transactions to calculate an overall reputation. Some sites only considerthe most recent ratings; however, it makes it impossible for users to know about the pastbehavior of the target user.

Reputation systems can be defined as systems that help people judge the reputation of others inorder to make better decisions about what to buy, who to listen to, or generally what to do.


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4.Existing reputation systems:Reputation systems form an important component of web-sites/services like eBay, Epinions andAmazon.com [2]. In transactions and interactions on the Internet and peer-to-peer systems,where there is a lack of other traditional indicators of trustworthiness, feedback from previous

customers plays a pivotal role [2].

Figure 9. Some existing Reputation Systems

Below is a brief study of web reputation systems in Amazon, eBay, ePinions, YouTube, facebook, twitter, email, wiki, and Google.

4.1. eBay: People are basically goodPeople are basically good is an article of faith at eBay, printed on the back of each employees

ID badge [4]. And in a sense, the company has the data to prove it: More than 99 percent of thetransactions completed on eBay result in a positive rating for both buyer and seller, and onlyabout 1 in 40,000 auctions ends in fraud [4]. EBay is perhaps the online worlds best argumentfor transparency and its own reputation is such that its very name adds credibility to eBay

inside companies such as Auction Drop that resell goods on eBay for individuals who are toobusy to trade themselves 4[4].

eBay reputation system was in early 2007, after an 11 year evolutionary process, before eBaychanged it significantly in the spring of 2007. In the conventional feedback (CF) system, both

traders (buyer and seller) of an eBay transaction could provide feedback on each other [3]. Thetrader could rate the transaction either as positive, neutral, or negative, and in additionalalso leave a text comment (e.g., item arrived as advertised). As soon as one of the traders

submits his feedback, it is published and made available to all other traders on eBay, even if theother trader involved in the transaction has not (yet) submitted his feedback. Once feedback wassubmitted, it could only be removed by court ruling, if the buyer did not pay, or if both traders

mutually agreed to a withdrawal [3]. The below figure provides a screenshot for how thereputation profile of a seller on eBay looked like in 2007.


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Figure 10. eBay Reputation System

The two most prominently displayed summary measures are the feedback score, equal to thenumber of positive feedback minus the number of negative feedback, and the positive feedback

equal to the percentage of positive feedback among the total feedback [3]. But in addition to theraw scores, much more information is being displayed, including a more fine-grained break-down of the feedback over the past month, past 6 month, and past 12 months, as well as the listof comments that have been submitted. Thus, a user contemplating bidding for an object that issold by this seller has a lot of information available to make his decision [3]. Many researchershave studied the eBay reputation system and found that traders do take the reputation intoaccount when making decisions. One study found that buyers are willing to pay an 8% premiumfor buying from a seller who has 2,000 positive feedbacks and 1 negative feedback, compared toa seller with 10 positive feedbacks and no negatives [3]. In another study, the researchers foundthat upon receiving his first negative rating, a sellers weekly sales drop significantly, but that

subsequent negative feedback has a much smaller effect. Furthermore, they find that sellers are

more likely to exit the market the lower their reputation is [3].


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Figure 11. eBay Feedback page

Ebays system is peer-to-peer, in the sense that buyers and sellers rate one another. By contrast,Epinions hosts a platform in which reviewers rate products and the reputation system, whichrates reviewers, raises the quality of and trust in the reviews. Customers have a betterunderstanding of the products they are buying and thus buy with greater confidence and are morelikely to get what they were expecting. Reputation systems work best when most peopleparticipate and contribute what they know, but that presents reputation system designers with a

trade-off. One reason eBay achieves high participation rates, is that it doesnt ask for much: justa positive or negative rating and maybe a comment.

4.2. Amazon:On Amazon.com, two different reputation systems are found:

I.The first one is for rating sellers for transactions where the product is not sold directly byAmazon, but by a third party on Amazons marketplace. The design and purpose of the

reputation system for the sellers is very similar to the one on eBay. Consider below figurewhich displays a screenshot for a seller on Amazon.com. As on eBay, buyers can rate a

transaction, but here they can use a rating between 1 star (worst) and 5 stars (best). These starsare then converted into a rating of positive (4-5 stars), neutral (3 stars), and negative (1-2 stars). However, the most prominently-displayed measure is the feedback rating, which isthe average star rating for this seller.


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Figure 12. The Amazon Reputation System for Sellers

II.The second reputation system on Amazon.com is for product reviews, i.e., where customersprovide feedback on the products they have bought directly from Amazon, addressing not themoral hazard problem but the adverse selection problem [3]. Similarly as for rating a seller,buyers can also rate a product on a 5-star scale, and leave a comment (or review) describingtheir experience about using the product. Next figure gives a screenshot for a computermonitor. This particular monitor has received 363 reviews [3]. The shading of the starsindicates the average star rating, although no numerical average is being displayed. However,the user can inspect the individual breakdown of the ratings, i.e., how many 1-star ratings, 2-

star ratings, etc., this product has received [3].

Figure 13. The Amazon Reputation System for Products

Amazon.com encourages users to create and share reviews in the multiple formats includingtexts, images and videos [1]. The earlier online reputation systems at Amazon tend to usescores/ratings to aggregate reputation information only. Now, Amazon accepts multimediafeedbacks including text reviews, customer images and videos, which complement the averagescore rating with richer information and help to reduce the misinterpretation of reputation scores.The past Amazon reputation systems are primarily product centered as it targets products beingsold. Reputation scores in the pre-Web 2.0 Amazon reputation systems are primarily about how


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good the product is to the customers [1]. As Web 2.0 movement emerges, Amazon has seriouslyexpanded its reputation system to embrace reviewers reputation. The reviewers profile now isdisplayed along each review he or she made. Reviewers are ranked based on three factors: thequality of the review, the correctness of the review, and total number of reviews the reviewer hascontributed. In Amazons current reputation system, the quality of the review plays the biggest

role in determining the reviewer ranking [1].

Table 1: Comparison of Amazon Pre-Web 2.0 and Web 2.0 Reputation systems

The quality of the review is primarily measured by how many members have voted the review asbeing helpful, which is displayed at the end of each review, readers of the review may easily

click on yes or no button to voice their votes [1]. In other words, Amazon partially relies ona simple voting based reputation system to evaluate reviewer reputation, beyond that, the morereviews a reviewer contribute, the more likely he/she will be ranked higher in Amazons top

reviewer list. Amazons reputation system allows members to revise their feedback if they makea mistake [1]. This feature is particularly helpful when the members experiences with using theproduct change over time, and would like to reflect those changes in his/her review [1].Amazons reputation system also includes a discussion forum where reviewers can comment onthe other reviewers post. Readers thus can easily easily voice their agreement / disagreement

with a reviewer, adding additional product information, or ask the reviewer additional questionsconcerning the product. They can even invite other customers who share similar interests aboutthe product to join the conversation [1].

Amazon Vs. eBay

Same sellers have poorer scores on Amazon than on eBay. This is due to the bidirectionalpossibility to evaluate sellers and buyers on eBay.

On eBay 60% of transactions result in feedback while only 12% on Amazon People on eBay want to increase their score thats why they leave more feedbacks eBay has no significant impact of feedback on demand and price while Amazon has 1%

increase positive feedback and Amazon can generate up to 3000$ more incomes overlong-term


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4.3. Epinions: A web of trustFounded in May of 1999 by Nirav Tolia and four others from Yahoo!,Netscape andExcite@Home, and launched with $8 million in funding from Benchmark Capital and AugustCapital, Epinions was one of the early efforts to aggregate word of mouth among shoppers on the

Internet [4]. Epinions took inspiration from Amazon for its user experience and user-generatedcontent, and from eBay as the best marketplace. They wanted people to rate the reviews and thereviewers, and to build a marketplace for content. Epinion offers product, merchant, review andreviewer ratings and tries to categorize products, merchants, etc [7]. Reviews consist of 1-to-5ratings and text ratings on many aspects such as ease of ordering, customer service, on-timedelivery etc. Reviews can be rated as not helpful, somewhat helpful, helpful or veryhelpful. The exact method for calculating reputation scores is unknown.

4.4. Face Book:The use of Social networking sites has increased remarkably over the years and enables us to

connect, keep in touch, share life experiences and also meet new people [9]. Statistics show thatFacebook has over 400 Million active users and at least 50 percent of them log on to Facebook inany given day. In addition an average user sends 8 friend requests per month [9]. On the internet,when users meet new people, they have no idea about what kind of person he/she is. Thus it ishard to trust and easy to get deceived [9]. A reputation system is hence required, to collectfeedback about users from their friends so as to calculate a reputation score that can be shown onpersonal profiles. This reputation score can help identify the frauds from the general pool ofusers, which will allow transactions (non-monetary, e.g. sharing important information) over theinternet to embrace a higher sense of assurance [9]. Reputation System of face book applicationmaintains complete anonymity between the reputation score provider and the reputation scoreconsumer. You can access the face book as well as embed it as part of your face book profile.

4.5. You tube:YouTube changed its Reputation System from a 5 star to Thumbs-up/Thumbs-down ratings [10].There is no doubt that the new design is better, but what bothers me is the way the ratinginformation is visualized to the users.

Figure 14. You Tube Reputation System


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In the most recent design, the video being played shows the number of likes and dislikes, whichis much better than the previous iterations which showed couple of grayed bars, and later coupleof colored bars [10]. Though the visualization has improved from bars to numbers, there is onemain problem i.e there is no absolute rating for the video. The Suggestion Videos column on the

right displays only thumbnails and the number of views. So a user has no way to know theauthenticity of the video without actually clicking on it. Since the users decision is based solelyon the thumbnail display and number of views, there is a good chance that a bad video becomesviral [10]. With appropriate weight-age for likes/dislikes and with proper standardization,absolute rating can be calculated and displayed with the video thumbnail. Or, the number oflikes/dislikes can be displayed with the thumbnail. But the problem with this is it adds lot ofvisual disturbance and it takes users time to process the information [10].

4.5.1. Issues [11]: Lack of trust in the rating system The ratings are highly biased

Too much noise in the comments because of the lack of moderation Lack of users' motivation for quality rating4.5.2. Key Recommendations [11]:

Provide specific and detailed information on the number of ratings Rearranging the text comments section Provide specific criteria for rating a video Provide filtering options based on specific criteria

Figure 15. You tube current design and re-design

The picture above shows a possible redesign solution that effectively uses the same space as theexisting design, but provides much richer information about the popularity of the video [11].

4.6. Email:As spam volumes have continued to increase with high rates, comprising 90% of all email by theend of 2006 as determined by Secure Computing Research, the need for fast and accuratesystems to filter the malicious email traffic and allow the good mail to pass through has providedgreater motivation for development of email reputation systems [5]. Traditional content filteringanti-spam systems can provide highly accurate detection rates but are usually prohibitively slowand poorly scalable to deploy in high-throughput enterprise and ISP environments [5].


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Reputation systems can provide more dynamic and predictive approaches to not only filter outthe unwanted mail but also identify the good messages, thus reducing the overall false positiverate of the system [5]. In addition, reputation systems allow for real-time collaborative sharing ofglobal intelligence about the latest email threats, providing instant protection benefits to the localanalysis that can be performed by a filtering system [5]. Email reputation systems have matured

over the years, but there remain open problems that can help fully achieve the broad role thatemail reputation systems can fulfill in securing messaging systems [5].

4.7. Yahoo Answers:In summer 2007, Yahoo! tried to address some moderation challenges with one of its flagshipcommunity products: Yahoo! Answers (answers.yahoo.com) [6]. The service had fallen victim toits own success and drawn the attention of trolls and spammers in a big way. The Yahoo!Answers team was struggling to keep up with harmful, abusive content that flooded the service,most of which originated with a small number of bad actors on the site [6]. Ultimately, theanswer to these woes was provided by a clever (but simple) system that was rich in reputation: it

was designed to identify bad actors, indemnify honest contributors, and take the overwhelmingload off of the customer care team [6].

Yahoo! Answers debuted in December of 2005 and almost immediately enjoyed massivepopularity as a community-driven website and a source of shared knowledge [6]. Yahoo!Answers provides a very simple interface to do, chiefly, two things: pose questions to a largecommunity (potentially, any active, registered Yahoo! user-that's roughly a half-billion peopleworldwide); or answer questions that others have asked. Yahoo! Answers was modeled, in part,from similar question-and-answer sites like Korea's Naver.com Knowledge Search [6]. Theappeal of this format was undeniable. By June of 2006, according toBusiness 2.0, Yahoo!Answers had already become the second most popular Internet reference site after Wikipedia

and had more than 90% of the domestic question-and-answer market share, as measuredby comScore. Its popularity continues and, owing partly to excellent search engineoptimization, Yahoo! Answers pages frequently appear very near the top of search results pageson Google and Yahoo! for a wide variety of topics [6]. Yahoo! Answers is by the most activecommunity site on the Yahoo! network. It logs more than 1.2 million user contributions(questions and answers combined) each day.

The questions asked and answers shared on Yahoo! Answers are often based on experiential

knowledge rather than authoritative, fact-based information [6].
http://answers.yahoo.com/http://answers.yahoo.com/


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Figure 16. Yahoo answers Reputation system

Yahoo! Answers, somewhat famously, already featured a reputation system-a very visible one,designed to encourage and reward ever-greater levels of user participation. On Yahoo! Answers,user activity is rewarded with a detailed point system [6].


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5.ConclusionE-commerce among strangers is solely possible through reputation systems. So, there is a need to

maintain a well equipped reputation system that can resist different kinds of collaborative

attacks. There is a need for a reputation system that provides strong incentives to players toremain honest under all circumstances. In this report we have compared famous and existingreputation systems of Amazon, eBay, ePinions, Facebook, Youtube, email and Yahoo answers.These all systems are facilitating users of web in different ways and styles. This is a quiteefficient system but still many enhancements can be done for secured reputation systems. But noone can deny the fact that the advent of reputation systems has contributed a lot in enhancingoverall quality of web and systems running on web.

Internet-based reputation systems, like traditional markets, aggregate vast amounts ofinformation, which then significantly influences choices made by businesses, as well as byindividuals. The parallel may end there. The theoretical underpinnings of the effective operation

of markets are well understood, and the aggregation to a brief set of statistics, namely a singleprice for each item, proceeds automatically. [3] In many ways, reputation systems are the on-lineequivalent of the body of laws that regulates the real-world interaction of people, and as such, weexpect that they will receive a growing amount of interest from practitioners and researchersalike. As the online world becomes increasingly the place where people interact and collaborate,we believe that the study of user reputation systems will be an important chapter of computerscience research.


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6.References[1]. Weijun Zheng, Online Reputation Systems in Web 2.0 Era, University of Wisconsin,Americans Conference of Information Systems AMCIS 2009 proceedings.

[2]. Rajat Bhattacharjee and Ashish Goel, Avoiding Ballot Stuffing in eBay-like ReputationSystems, Stanford University, ACM 2005

[3]. David C. Parkes, Sven Seuken, CS 186 Lecture 16 | Reputation Systems, October 24,2011

[4]. Online Reputation Systems, Esther Dysons monthly report, volume 21, No. 9, 23October 2003,www.edventure.com

[5]. Dmitri Alperovitch, Paul Judge, and Sven Krasser, Taxonomy of Email ReputationSystems, Secure Computing Corporation

[6]. http://buildingreputation.com/doku.php?id=chapter_10

[7]. Hui Li, A Configurable Online Reputation Aggregation System, University of Ottawa,Canada, 2007

[8]. James L. Horton, Reputation Systems and the Internet

[9]. http://www.infosysblogs.com/web2/2010/03/reputation_system_application.html

[10]. http://umanka.wordpress.com/2010/12/14/youtube-redesign-reputation-system

[11]. http://www.pausalisen.com/YouTube/you.pdf

[12]. Audun Jsang, Roslan Ismail, Colin Boyd, A Survey of Trust and Reputation Systemsfor Online Service Provision, University Tenaga National (UNITEN), Malaysia, Published inDecision Support Systems, 2007, p.618-64

[13]. Audun Josang, Online Reputation System for the Health Sector, Queensland Universityof Technology, Australia, Journal of Health Informatics. 2008, Vol 3(1): e8. http://www.ejhi.net

[14]. Milad Sharif and Soheil Norouzi, Sentiment Based Model For Reputation Systems In

Amazon

[15]. Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The PageRankCitation Ranking: Bringing Order to the Web. Technical report, Stanford Digital LibraryTechnologies Project, 1998.
http://www.edventure.com/http://www.edventure.com/http://www.edventure.com/http://buildingreputation.com/doku.php?id=chapter_10http://www.infosysblogs.com/web2/2010/03/reputation_system_application.htmlhttp://umanka.wordpress.com/2010/12/14/youtube-redesign-reputation-systemhttp://www.pausalisen.com/YouTube/you.pdfhttp://www.ejhi.net/http://www.ejhi.net/http://www.pausalisen.com/YouTube/you.pdfhttp://umanka.wordpress.com/2010/12/14/youtube-redesign-reputation-systemhttp://www.infosysblogs.com/web2/2010/03/reputation_system_application.htmlhttp://buildingreputation.com/doku.php?id=chapter_10http://www.edventure.com/

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