Towards a Global Online Reputation

Transcription

1 Hu L Unversty of Ottawa 55 Laurer Ave E Ottawa, ON KN 6N5 Canada + (63) , 8834 Hl03@uottawa.ca Towards a Global Onlne Reputaton Morad Benyoucef Unversty of Ottawa 55 Laurer Ave E Ottawa, ON KN 6N5 Canada + (63) , 4787 benyoucef@telfer.uottawa.ca Gregor v. Bochmann Unversty of Ottawa 88 Kng Edward Ave Ottawa, ON KN 6N5 Canada + (63) , 6205 bochmann@ste.uottawa.ca ABSTRACT Today s onlne reputaton systems lack one mportant feature: globalty. Users buld a reputaton wthn one communty, and sometmes several reputatons wthn several communtes, but each reputaton s only vald wthn the correspondng communty. Moreover, such reputaton s usually aggregated by the onlne platform s provder, gvng the nqurng agent no say n the process. Ths paper proposes one way of dealng wth ths problem. e ntroduce an onlne reputaton centralzer that collects raw reputaton data about users from several onlne communtes and allows for t to be aggregated accordng to the nqurng agent s requrements, usng a stochastc trust model, and takng nto account factors that qualfy a user s reputaton. Categores and Subject Descrptors C.2.0 [Computer-Communcaton Networks]: Securty and Protecton; H.3.5 [Onlne Informaton Servces]: eb-based servces. Keywords Reputaton System, Onlne Trust, Stochastc Model.. INTRODUCTION The Internet has enabled the prolferaton of onlne nterpersonal and busness nteractons between ndvduals who have never nteracted before. These nteractons are usually completed wth some concern gven that prvate nformaton and the exchange of money and goods are nvolved. A mechansm s therefore needed to buld trust among strangers who nteract onlne. Trust can be dvded nto drect and recommender trust. hle drect trust comes from drect experence, recommender trust s derved from word-of-mouth recommendatons []. Trust s dynamc and can be developed over tme as the outcome of observatons leadng to the belef that the actons of another may be reled upon [3]. One way to foster trust n onlne nteractons s through collectng and managng nformaton about the past behavour of Permsson to make dgtal or hard copes of all or part of ths work for personal or classroom use s granted wthout fee provded that copes are not made or dstrbuted for proft or commercal advantage and that copes bear ths notce and the full ctaton on the frst page. To copy otherwse, or republsh, to post on servers or to redstrbute to lsts, requres pror specfc permsson and/or a fee. Conference Copyrght nteractng partes. Ths nformaton s then aggregated nto an entty called reputaton. Reputaton s defned as a collectve measure of trustworthness based on the ratngs of communty members [2] whch mght affect the nteractng party s future payoffs [4]. Onlne reputaton systems are communty tools that collect, dstrbute, and aggregate feedback about partcpants past behavour [5]. A negatve reputaton system gathers and dstrbutes feedback on untrustworthy partcpants to dscourage ther behavour; whle a postve reputaton system encourages partcpants wth a hstory of honest behavour [6]. In a hybrd reputaton system, both postve and negatve behavours are taken nto account. In such a case, partcpants start wth neutral reputaton values, then ponts are taken away as a punshment for bad behavour or added as a reward for good behavour [7]. EBay s feedback forum ( s an example of a hybrd reputaton system. It allows partcpants to rate each other wth + for postve, 0 for neutral, and - for negatve feedback. All the feedback values are then aggregated nto one reputaton value to be consulted by members of the ebay communty [2]. Three enttes are usually nvolved n trust models for onlne reputaton systems: () the queryng agent, who s the user who wants to know whether a gven user (the ratee) can be trusted; (2) the ratee, who s rated by others on hs/her past behavour; and (3) the rater, also called recommender, who s the user provdng nformaton about the ratee, usually after havng nteracted wth hm/her. Onlne reputaton systems rase numerous challengng research questons [4]. In ths paper we address one of them: the lack of globalty. It s ndeed dffcult to exchange reputaton data between dfferent onlne reputaton systems [5]. A member of the ebay communty, for nstance, cannot use hs/her reputaton outsde the ebay communty - hence the name local reputaton. It s desrable that a user who has a good reputaton wthn one communty could use hs/her reputaton wthn other communtes - hence the name global reputaton. As a step towards globalty, we suggest the aggregaton of reputaton data from dfferent onlne communtes. A major dffculty s that each communty calculates reputaton dfferently. For nstance, a ratng value on ebay s between - and whle other onlne communtes use ratngs between 0 and 5 and may nclude textual comments as well. In order to aggregate reputaton data from varous communtes, we propose

2 a common reputaton model nto whch the data can be translated. thn a global vew of onlne reputaton, a ratee grants permsson (wth the possblty of optng out) to the communtes where he/she has developed a reputaton to share hs data wth a global aggregaton servce. e envson such a servce to be offered by a thrd party who partners wth onlne communtes. The busness, prvacy, and securty mplcatons of an aggregaton servce are undoubtedly mportant but they are beyond the scope of ths paper. Before nteractng wth the ratee, a user (the queryng agent) logs nto the aggregaton servce, looks up the ratee, gets access to hs/her raw reputaton data from partnerng onlne communtes, and confgures the aggregaton process. Ths process can be confgured by parameters such as: weghts assgned to onlne communtes (perhaps gvng more weght to the more establshed communtes), transacton dates (perhaps gvng more weght to the more recent recommendatons), transacton values (perhaps gvng more weght to recommendatons for hgh value transactons), etc. Instead of provdng a dead reputaton score (as most of today s onlne reputaton systems - e.g., ebay s feedback forum), the queryng agent s gven the opportunty to be nvolved by confgurng the aggregaton process and thus wll fnd the aggregated feedback more useful. The rest of the paper s organzed as follows. Secton 2 revews dscrete reputaton models. Secton 3 detals our proposed reputaton model. Secton 4 reports on an mplementaton of the proposed model. Secton 5 revews related work and contrasts t wth our proposed soluton, and Secton 6 concludes the paper. 2. DISCRETE REPUTATION MODELS Computaton models for onlne reputaton can be classfed nto summaton, weghted average, fuzzy, flow, Bayesan, belef, and dscrete models [2]. e only dscuss dscrete models here. Usng a dscrete model such as the one n [3], a rater evaluates hs/her nteracton wth the ratee as excellent, good, normal, bad or worst. One shortcomng of dscrete models s that they are not precse snce heurstcs mechansms le lookup tables must be used [2] to convert feedback values nto ther numerc equvalent. In [4] dscrete feedback s used n conjuncton wth a statstcal model to compute trust based on self-experence and recommendatons from raters. It s assumed that the space of possble outcomes of transactons s fnte (e.g., excellent, very good, good and bad ) and that N transactons have been observed for the same ratee by the queryng agent or other raters. Assumng that ratee b wll perform n a smlar manner n the future, one can predct the probablty of the dfferent outcomes for future transactons usng the formula: T b(o) (number of tmes the observed outcome was equal to o) / N. T b(o) s the probablty that a future transacton wth ratee b wll lead to an outcome o. The sum of the values T b(o) over all values of o yelds the value one. T b(o) s also called the trust that ratee b wll provde an outcome o. Instead of keepng all prevous transacton outcomes n memory, an ncremental trust update formula s used [4]. The current trust T b(o) (for each value of o) and the number of observatons to date are kept n memory, and after a new transacton yeldng outcome o s observed, the trust values and N wll be updated as follows: T b(o) (T b(o)*n +)/ (N+). T b(o ) (T b(o )*N )/ (N+) for o dfferent from o. NN+. Note that a multdmensonal reputaton model can be consdered n the context of dscrete reputaton. For nstance, a seller s reputaton can be evaluated accordng to two dmensons: qualty of good and servce. For both dmensons, one may set up dscrete values for the possble outcomes, such as excellent, good, etc. 3. PROPOSED REPUTATION MODEL Our approach frst aggregates a ratee s local reputatons then combnes them nto a global one. A ratee s local reputaton s lnked to a sngle communty (e.g., a seller s reputaton on ebay s consdered local to the ebay communty). If a communty mantans an onlne reputaton system, then the ratee s rated every tme he/she transacts wthn that communty. Note that we are not nterested n the aggregated reputaton value as provded by the communty s reputaton system but rather n the raw data. Let us assume that the raw data s comprsed of the followng elements. - Feedback value: ths s an essental parameter n reputaton models (also called ratng or recommendaton). Ths value s typcally gven by a rater as feedback on a sngle transacton wth the ratee. Reputaton systems dffer n ther feedback representaton formats, whch could be dscrete or contnuous; numercal or textual or both. Some systems use feedback values alone to aggregate a user s reputaton wthout consderng other attrbutes (e.g., ebay only sums up the feedback values). - Informaton on rater credblty: the qualty of recommendatons n trust systems s not guaranteed snce nothng prevents malcous raters from provdng unfar recommendatons. As stated n [8, 9, 0, 6], feedback from raters wth hgher credblty should be weghted more than feedback from those wth lower credblty snce these are more lely to submt dshonest feedback. However determnng rater credblty s a challenge. Sh et al. [5] for nstance use data analyss and machne learnng technques to detect unfar recommendatons. The queryng agent may also compare the recommendaton wth hs own experence. If the queryng agent decdes to nteract wth a ratee based on a recommendaton from a rater, the dfference between the rater s and the queryng agent s perceptons, called semantc dstance [], can be used to adjust future recommendatons from the same rater. In [9], raters credblty s a functon of ther reputaton wthn the communty, hence reputable raters are consdered more credble, and therefore ther ratngs wegh more. - Context factor: varous transacton parameters such as the sze and tme of a transacton can be consdered, for nstance the feedback for larger and more recent transactons may be assgned more weght. More recent transactons are lely to better reflect the current behavour of the ratee [2, 6]. The sze of the

3 transacton [6] s consdered n order to avod the stuaton where a user behaves honestly for small transactons and dshonestly for larger ones. - Number of transactons: the number of transactons s useful because the total feedback dvded by the number of transactons reflects a ratee s reputaton better than the total feedback alone. It s mportant to note that other elements can be part of the raw reputaton data hence t should not be lmted to the elements mentoned above. Some reputaton models, for nstance, consder that the longer a rater has been part of a communty, the more weght should be gven to hs/her feedback on other members. Others value the feedback of raters wth the most transactons (regardless of how long they have been n the communty). For more on ths topc, the reader s referred to [4]. e assume dscrete feedback s used. For nstance ebay uses the dscrete values, 0 and - to stand for Postve, Neutral, and Negatve. Dscrete feedback needs to be normalzed, so normalzng the three ebay dscrete values wthn the range [0, ] would yeld the numercal values, 0.5 and 0. Unfortunately, normalzaton could lead to unrealstc results. For nstance, one ratee may have fve Postve (), and fve Negatve (0) transactons, whle another may have ten Neutral (0.5) transactons. If every feedback s equally weghted, these two ratees would end up wth the same reputaton value (namely 0.5), whch does not reflect the realty. For that reason, we decded to follow a dfferent approach nspred by Sh et al. [4]. In order to represent dscrete reputaton better, we propose a stochastc trust model based on the assumpton that the ratee behaves le a stochastc process, and the reputaton value represents the expectaton that the ratee wll act accordngly n the future (see Secton 2). e calculate (Formulas, 2, 3) the estmated probablty of each possble dstnct outcome ( Postve, Neutral, or Negatve ) for the acton of the ratee takng nto account the dfferent ratng attrbutes ntroduced earler. e then sum up these values together wth the correspondng numercal value (representng that outcome) (Formula 4). The aggregated reputaton of ratee denoted by R s calculated usng the followng formulas: I ( ) P ( o) () I ( ) k, f o m CR * CF (2) CF a* T + b* S c., b, c [0, ]& a+ b+ c + a (3) R P ( o)* NumVal( o) (4) o O Here: P (o) the estmated probablty that ratee wll provde the outcome o n the future; O the set of possble outcomes, such as excellent, good, average, bad, and very bad ; I() the total number of transactons; f ratee s feedback value for transacton k; the aggregaton weght for ratee s feedback value for transacton k; CR the credblty of the rater who rated ratee for transacton k (note that ratee can be rated many tmes by the same rater, but we only consder the rater s reputaton at the moment transacton k s performed); CF the context factor for ratee s feedback value for transacton k; T the tme context factor for ratee s feedback value for transacton k; S the sze context factor for ratee s feedback value for transacton k; NumVal(o) the numercal value correspondng to the outcome o (usng a lookup table). Table. Feedback values, ther correspondng f and aggregaton weghts K f Postve 2 Neutral Negatve Postve 5 Neutral 6 Negatve 7 Postve 8 Postve Neutral 0 Postve 0.3 For an llustraton, consder the example of a ratee wthn a communty X who has been rated 0 tmes (.e., I() 0) possbly more than once by the same rater. Table shows the 0 feedback values as well as ther correspondng f, and the aggregaton weghts for each feedback value. Table 2 shows the mappng of dscrete values nto numercal values. Table 2. Lookup Table Dscrete Numercal Postve Neutral 0.5 Negatve 0 The estmated probablty of ratee beng Postve, Neutral or Negatve n future transactons can be calculated as follows: 0 P ( Postve ) 0 k, f Postve 0 P ( Neutral ) 0 k, f Neutral 0 P ( Negatve ) 0 k, f Negatve m m m m The local reputaton of ratee wthn communty X has a value of as estmated below R P ( o)* NumVal( o) *+ *0.5 + * o O In order to apply the computaton model, the attrbutes that serve n the aggregaton need to be normalzed. Reputaton systems mantaned by dfferent onlne communtes use dfferent formats to represent these attrbutes. Before aggregatng them, t s necessary to normalze them nto numercal values usng mappng tables or converson formulas as proposed n []

4 After the local reputatons for every onlne communty have been calculated they are aggregated nto a global reputaton. The global reputaton (GR ) s calculated as follows: GR I ( j) j Rj * I ( j ) j m Here: R j local reputaton for ratee wthn communty j; j the aggregaton weght for communty j; I(j) the number of communtes consdered. Note that assgnng a weght of zero to a communty dscards t from the global reputaton aggregaton. e note that we assume here that a ratee can be globally dentfed throughout all communtes. However, the raters only need to be ndentfed wthn ther communty where ther credblty s supposed to be known. The same rater may occur n dfferent communtes wth dfferent dentfers and dfferent local credbltes. 4. IMPLEMENTATION e mplemented and tested the reputaton model descrbed above n the form of an Onlne Reputaton Aggregaton System (ORAS). The system s composed of the followng components: User Interface, Admnstrator Interface, Aggregaton Module, Mappng Module and Lookup Tables (see Fgure for the archtecture). The User Interface can be used by queryng agents to regster, enter the dentty of the ratee to be looked up, select the ratng attrbutes, ther weghts, etc. Through the User Interface, the queryng agent can select the confguraton parameters for the aggregaton process, such as the values of a (mportance of tme context factor) and b (the mportance of the sze context factor), and for each communty j ncluded n the aggregaton, the weght j for the reputaton n that communty and the lookup table NumVal j contanng the numercal values of the dfferent outcomes consdered n that communty. The Admnstrator Interface can be used to setup Lookup Tables, calculaton algorthms, mappng schemes, converson parameters, etc. The Aggregaton Module mplements the algorthms used to compute the local reputaton for every communty as well as the global reputaton. The Mappng Module normalzes raw reputaton data nto a common format usng Lookup Tables. Fnally, partcpatng Onlne Communtes create and expose eb Servces that gve access to the raw Reputaton Data of the ratees (and only those) who have granted them permsson to do so. Fgure. Conceptual Archtecture of ORAS Fgure 2 shows how the user Hu@mal.com selects the communtes (named X, Y, Z n ths example) she wants to consder n her calculaton. Remember that these communtes are partnerng wth the aggregaton servce, and that the ratee n queston (dentfed as Alex@mal.com n ths example) has agreed to hs data beng shared wth the aggregaton servce. In ths example, the user assgns the hghest weghts to the communtes beleved to be the most accurate n reflectng the real reputaton of the ratee. Fgure 2. User Interface (Step ) In Step 2, the user chooses the ratng attrbutes and sets ther weghts. In the current mplementaton of ORAS, the credblty of a rater s taken to be the value of hs/her own reputaton at the moment he/she provded the feedback. Other methods for computng rater credblty can be mplemented. Fgure 3 shows the output screen after ORAS computes the local and global reputaton values for ratee Alex@mal.com.

5 Fgure 3. Local and global reputatons are dsplayed 5. RELATED ORK EgoSphere [7] s a reputaton system amng to ntegrate dfferent reputaton servces by facltatng the transfer of reputaton between them. It s composed of three modules: a web proxy, a reputaton database and a reputaton exchange. The web proxy runs on a user s computer montorng all reputaton-related actvtes. It fetches the webpage requested by the user from an EgoSphere-supported server, and analyzes the HTML code searchng for reputaton evdence and EgoSphere annotatable content such as usernames. The reputaton database receves and manages the reputaton evdence from many web proxy sources. The reputaton exchange uses such evdence to calculate how much reputaton data should be transferred from one servce to another. The basc dea s that the more smlarty two servces have, the more reputaton evdence can be transferred from one to the other. Our soluton s dfferent n that the sharng of reputaton nformaton s condtoned by the user s approval, and our system does not need to parse HTML code because t has access to the raw data from partcpatng onlne communtes. Commercal applcatons are beng launched by onlne busnesses (many of them start-ups) attemptng to offer centralzed reputaton servces, among them Karma ( and authorat ( The Authorat ratng servce offers bloggers and onlne artcle authors a way to gan reputaton and ncrease the vsblty of ther publshng. Users are allowed to lst the URLs of ther blogs/artcles on ther Authorat pages after regstraton. Readers can then rate the blogs/artcles on Authorat. The ratng conssts of two parts: the authorty ratng (scale of -5) and the authorshp ratng (scale of -0). Each averaged ratng wll be shown below a blog or an artcle. Authorat allows readers to tag the contents of blogs/artcles n felds such as arts, busness, sports, technology, scence, entertanment etc. In order to provde a portable ratng servce for blogs and onlne artcles, Authorat offers ts members a servce for addng web wdgets nto ther blogs or web pages to dsplay the Authorat ratngs. Members smply copy a pece of HTML code that generates the web wdget and paste t on ther blog, web page, or anywhere they want to show ther Authorat ratngs. Usng a process that s more or less smlar to Authorat, the Karma onlne reputaton servce enables ts members to rate other people and busness. The dea s to provde a central locaton for managng reputaton. In other words, when I nteract wth user U on webste, nstead of ratng hm/her on webste, I go to a reputaton centralser (e.g., Karma, Authorat) and enter my ratngs there. Typcally, I can also clck on user U s badge/wdget (f dsplayed on webste ) to see hs/her current reputaton. hat we propose here s fundamentally dfferent from what s currently offered by commercal servces. Our soluton () deals wth raw reputaton data; (2) offers the possblty to aggregate the local and global reputaton accordng to the rater s specfcatons; (3) offers the possblty to select what communtes (ndvdual webstes) to nclude n the aggregaton process; and (4) provdes a more confgurable aggregaton process for reputaton. The am however remans the same: the portablty, centralzaton, and globalzaton of onlne reputaton. 6. CONCLUSION Ths paper addressed the lack of globalty n onlne reputaton systems. Users who buld a reputaton n one communty are unable to transfer t to another communty. In vew of the mportance that reputaton systems are ganng as a way of fosterng trust n onlne busness and nterpersonal nteractons, we beleve globalty to be an mportant feature. Our approach to acheve t s to gather raw reputaton data about a ratee from varous communtes, aggregate the data from a gven communty nto what we call a local reputaton, then aggregate all local reputaton values nto a global reputaton. The aggregaton s based on optons and weghts whch are selected by the nqurng agent accordng to hs/her personal requrements. Our computaton algorthm s based on a statstcal model whch takes nto account several factors and parameters that qualfy the reputaton. A prototype based on the proposed model has been mplemented and tested. The next step s to valdate the model usng real and/or smulated recommendaton data. Several extensons are envsaged for ths work, among them: () consderng reputaton to be multdmensonal where a ratee can be rated on more than one ssue (product qualty, servce, etc.); (2) consderng other factors n the aggregaton of local reputaton; and (3) nvestgatng other ways to calculate the raters credblty. 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