Building Trust On-Line: The Design of Reliable Reputation Reporting Mechanisms for Online Trading Communities

Transcription

1 A reearch and education initiative at the MIT Sloan School of Management Building Trut On-Line: The Deign of Reliable Reputation Reporting Mechanim for Online Trading Communitie Paper 101 Chryantho Dellaroca July 2001 For more information, pleae viit our webite at or contact the Center directly at or

2 Building Trut On-Line: The Deign of Reliable Reputation Reporting Mechanim for Online Trading Communitie Chryantho Dellaroca Sloan School of Management Maachuett Intitute of Technology Room E Cambridge, MA Abtract: Several propertie of online interaction are challenging the accumulated widom of trading communitie on how to produce and manage trut. Online reputation reporting ytem have emerged a a promiing trut management mechanim in uch etting. The objective of thi paper i to contribute to the contruction of online reputation reporting ytem that are robut in the preence of unfair and deceitful rater. The paper et the tage by providing a critical overview of the current tate of the art in thi area. Following that, it identifie a number of important way in which the reliability of the current generation of reputation reporting ytem can be everely compromied by unfair buyer and eller. The central contribution of the paper i a number of novel immunization mechanim for effectively countering the undeirable effect of uch fraudulent behavior. The paper decribe the mechanim, prove their propertie and explain how variou parameter of the marketplace microtructure, mot notably the anonymity and authentication regime, can influence their effectivene. Finally, it conclude by dicuing the implication of the finding for the manager and uer of current and future electronic marketplace and identifie ome important open iue for future reearch. 1. Introduction At the heart of any bilateral exchange there i a temptation, for the party who move econd, to defect from the agreed upon term in way that reult in individual gain for it (and loe for the other party). For example, in tranaction where the buyer pay firt, the eller i tempted to not provide the agreed upon good or ervice or to provide them at a quality which i inferior to what wa advertied to the buyer. Unle there are ome other guarantee, the buyer would then be tempted to hold back on her ide of the exchange a well. In uch ituation, the trade will never take place and both partie will end up being wore off. Unecured bilateral exchange thu have the tructure of a Prioner Dilemma. Our ociety ha developed a wide range of informal mechanim and formal intitution for managing uch rik and thu facilitating trade. The imple act of meeting face-to-face to ettle a tranaction help reduce

3 the likelihood that one party will end up empty-handed. Written contract, commercial law, credit card companie and ecrow ervice are additional example of intitution with exactly the ame goal. Although mechanim deign and intitutional upport can help reduce tranaction rik, they can never eliminate them completely. One example i the rik involving the exchange of good whoe real quality can only be aeed by the buyer a relatively long time after a trade ha been completed (e.g. ued car). Even where ociety doe provide remedial meaure to cover rik in uch cae (for example, the Maachuett lemon law ), thee are uually burdenome and cotly and mot buyer would very much rather not have to reort to them. Generally peaking, the more the two ide of a tranaction are eparated in time and pace, the greater the rik. In thoe cae, no tranaction will take place unle the party who move firt poee ome ufficient degree of trut that the party who move econd will indeed honor it commitment. The production of trut, therefore, i a precondition for the exitence of any market and civilized ociety in general (Dunn, 1984; Gambetta, 1990). In brick and mortar communitie, the production of trut i baed on everal cue, often rational but ometime purely intuitive. For example, we tend to trut or ditrut potential trading partner baed on their appearance, the tone of their voice or their body language. We alo ak our already truted partner about their prior experience with the new propect, under the aumption that pat behavior i a relatively reliable predictor of future behavior. Taken together, thee experience form the reputation of our propective partner. The emergence of electronic market and other type of online trading communitie are changing the rule on many apect of doing buine. Electronic market promie ubtantial gain in productivity and efficiency by bringing together a much larger et of buyer and eller and ubtantially reducing the earch and tranaction cot (Bako, 1997; Bako, 1998). In theory, buyer can then look for the bet poible deal and end up tranacting with a different eller on every ingle tranaction. None of thee theoretical gain will be realized, however, unle market maker and online community manager find effective way to produce trut among their member. The production of trut i thu emerging a an important management challenge in any organization that operate or participate in online trading communitie. Several propertie of online communitie challenge the accumulated widom of our ocietie on how to produce trut. Formal intitution, uch a legal guarantee, are le effective in global electronic market, which pan multiple juridiction with, often conflicting, legal ytem (Johnon and Pot, 1996). For example, it i very difficult, and cotly, for a buyer who reide in the U.S.A. to reolve a trading dipute with a eller who live in Indoneia. The difficulty i compounded by the fact that, in many electronic market, it i relatively eay for trading partner to uddenly diappear and reappear under a different online identity (Friedman and Renick, 1999; Kollock, 1999).

4 Furthermore, many of the cue baed on which we tend to trut or ditrut other individual are abent in electronic market where face-to-face contact i the exception. Finally, one of the motivating force behind electronic market i the deire to open up the univere of potential trading partner and enable tranaction among partie who have never worked together in the pat. In uch a large trading pace, mot of one already truted partner are unlikely to be able to provide much information about the reputation of many of the other propect that one may be conidering. A a counterbalance to thoe challenge, electronic communitie are capable of toring complete and accurate information about all tranaction they mediate. Several reearcher and practitioner have, therefore, tarted to look at way in which thi information can be aggregated and proceed by the market maker or other truted third partie in order to help online buyer and eller ae each other trutworthine. Thi ha lead to a new breed of ytem, which are quickly becoming an indipenable component of every ucceful online trading community: electronic reputation reporting ytem. We are already eeing the firt generation of uch ytem in the form of online rating, feedback or recommender ytem (Renick and Varian, 1997; Schafer et.al., 2001). The baic idea i that online community member are given the ability to rate or provide feedback about their experience with other community member. Feedback ytem aim to build trut by aggregating uch rating of pat behavior of their uer and making them available to other uer a predictor of future behavior. ebay ( for example, encourage both partie of each tranaction to rate one another with either a poitive (+1), neutral (0) or a negative (-1) rating plu a hort comment. ebay make the cumulative rating of it member, a well a all individual comment publicly available to every regitered uer. The majority of the current generation of online feedback ytem have been developed by Internet entrepreneur and their reliability ha not yet been ytematically reearched. In fact, there i ample anecdotal evidence, a well a one recent legal cae i, related to the ability to effectively manipulate people action by uing online feedback forum (tock meage board in thi cae) to pread fale opinion. A more and more organization participate in electronic marketplace, online reputation reporting ytem deerve new crutiny and the tudy of trut management ytem in digital communitie deerve to become a new addition to the burgeoning field of Management Science. The objective of thi paper i to contribute to the contruction of online reputation reporting ytem that are robut in the preence of unfair and deceitful rater. The paper et the tage by providing a critical overview of the current tate of the art in thi area (Section 2). Following that, it identifie a number of important way in which the predictive value of the current generation of reputation reporting ytem can be everely compromied by unfair buyer and eller (Section 3). The central contribution of the paper i a number of novel immunization mechanim for effectively countering the undeirable effect of uch fraudulent behavior. The paper decribe the mechanim, prove their propertie and explain how variou parameter of the marketplace microtructure, mot notably the anonymity and authentication regime, can

5 influence their effectivene (Section 4). Finally, it conclude by dicuing the implication of the finding for the manager and uer of current and future electronic marketplace and identifie ome open iue for future reearch (Section 5). 2. Reputation reporting mechanim in online communitie The relative eae with which computer can capture, tore and proce huge amount of information about pat tranaction, make pat behavior (reputational) information a particularly promiing way on which to bae the production of trut in online communitie. Thi fact, together with the fact that the other traditional way of producing trut (intitutional guarantee, indirect cue) do not work a well in cyberpace, ha prompted reearcher and practitioner to focu their attention on developing online trut building mechanim baed on reputational information. Thi ection provide a critical urvey of the tate-of-the-art in thi field. A reputation, a defined by Wilon (Wilon, 1985) i a characteritic or attribute acribed to one peron by another. Operationally, thi i uually repreented a a prediction about likely future behavior. It i, however, primarily an empirical tatement. It predictive power depend on the uppoition that pat behavior i indicative of future behavior. Reputation ha been the object of tudy of the ocial cience for a long time (Schmalenee, 1978; Shapiro, 1982; Smallwood and Conlik, 1979). Several economit and game theorit have demontrated that, in the preence of imperfect information, the formation of reputation i an important force that help buyer manage tranaction rik, but alo provide incentive to eller to provide good ervice quality. Having interacted with omeone in the pat i, of coure, the mot reliable ource of information about that agent reputation. But, relying only on direct experience i both inefficient and dangerou. Inefficient, becaue an individual will be limited in the number of exchange partner he or he ha and dangerou becaue one will dicover untrutworthy partner only through hard experience (Kollock, 1999). Thee hortcoming are epecially evere in the context of online communitie where the number of potential partner i huge and the intitutional guarantee in cae of negative experience are weaker. Great gain are poible if information about pat interaction i hared and aggregated within a group in the form of opinion, rating or recommendation. In the brick and mortar communitie thi can take many form: informal goip network, intitutionalized rating agencie, profeional critic, etc. In cyberpace, they take the form of online reputation reporting ytem, alo known a online recommender ytem (Renick and Varian, 1997). The following ection provide a brief dicuion of the mot important deign challenge and categorie of thee ytem. 2.1 Deign challenge in online reputation reporting ytem

6 Although the effective aggregation of other community member opinion can be a very effective way to gather information about the reputation of propective trading partner, it i not without pitfall. The following paragraph decribe two important iue that need to be addreed by opinion-baed reputation reporting mechanim: Subjectively meaurable attribute. In the ret of the paper we will ue the term agent to refer to a participant (buyer or eller, human or oftware) of an online trading community. We ay that an attribute Q of an agent i ubjectively meaurable if identical behavior of agent vi-à-vi two different agent b1 2 and b may reult in two different rating R b Rb 1 2 for attribute Q by the repective rater. The mot common example of a ubjectively meaurable attribute i the notion of product or ervice quality. In mot tranaction type, ome of the attribute of interet are ubjectively meaurable. In order for agent b to make ue of other agent rating for ubjectively meaurable attribute a a bai for calculating agent reputation, it mut firt try to tranlate each of them into it own value ytem. In traditional communitie we addre the above iue by primarily accepting recommendation from people whom we know already. In thoe cae, our prior experience with thee people help u gauge their opinion and tranlate them into our value ytem. For example, we may know from pat experience that Bill i extremely demanding and o a rating of acceptable on hi cale would correpond to brilliant on our cale. A a further example, we may know that Mary and we have imilar tate in movie but not in food, o we follow her opinion on movie while we ignore her recommendation on retaurant. Due to the much larger number of potential trading partner, in online communitie it i, once again, le likely that our immediate friend will have had direct experience with everal of the propect conidered. It i, therefore, more likely that we will have to rely on the opinion of tranger o gauging uch opinion become much more difficult. Intentionally fale opinion For a number of reaon (ee Section 3) agent may deliberately provide fale opinion about another agent, that i, opinion, which bear no relationhip to their truthful aement of their experience with that other agent. In contrat to ubjective opinion, for which we have aumed that there can be a poibility of tranlation to omebody ele value ytem, fale opinion are uually deliberately contructed to milead their recipient and the only enible way to treat them i to ignore them. In order to be able to ignore them, however, one ha to firt be able to identify them. Before accepting opinion, rater mut, therefore, alo ae the trutworthine of other agent with repect to giving honet opinion. (Yahalom et. al., 1993) correctly pointed out that the o-called recommender trutworthine of an agent i

7 orthogonal to it trutworthine a a ervice provider. In other word, an agent can be a high-quality ervice provider and a very unreliable recommendation provider or vice vera. In the ret of the ection we will briefly urvey the variou clae of propoed online reputation reporting ytem and will dicu how each of them fare in addreing the above iue. 2.2 Recommendation repoitorie Recommendation repoitorie tore and make available recommendation from a large number of community member without attempting to ubtantially proce or qualify them. The Web i obviouly very well uited for contructing uch repoitorie. In fact, mot current-generation web-baed recommendation ytem (meage board, opinion forum, etc.) fall into thi category. A typical repreentative of thi cla of ytem i the feedback mechanim of auction ite ebay. Other popular auction ite, uch a Yahoo and Amazon employ very imilar mechanim. ebay encourage the buyer and eller of an ebay-mediated tranaction to leave feedback for each other. Feedback conit of a numerical rating, which i can be +1 (praie), 0 (neutral) or 1 (complaint) plu a hort (80 character max.) text comment. ebay then make the lit of all ubmitted feedback rating and comment acceible to any other regitered uer of the ytem. ebay doe calculate ome rudimentary tatitic of the ubmitted rating for each uer (the um of poitive, neutral and negative rating in the lat 7 day, pat month and 6 month) but, otherwie, it doe not filter, modify or proce the ubmitted rating. Recommendation repoitorie are a tep in the right direction. They make lot of information about other agent available to intereted uer, but they expect uer to make ene of thoe rating themelve and draw their own concluion. On the one hand, thi i conitent with the viewpoint that the aement of omebody trutworthine i an eentially ubjective proce (Boon and Holme, 1991). On the other hand, however, thi baeline approach doe not cale very well. In ituation where there are dozen or hundred of, poibly conflicting, rating, uer need to pend coniderable effort reading between the line of individual rating in order to tranlate other people rating to their own value ytem or in order to decide whether a particular rating i honet or not. What more, in communitie where mot rater are complete tranger to one another, there i no concrete evidence that reliable reading between the line i poible at all. In fact, a we mentioned, there i ample anecdotal evidence of people being miled by following the recommendation of fale meage poted on Internet feedback forum. 2.3 Profeional (pecialit) rating ite Specialit-baed recommendation ytem employ truted and knowledgeable pecialit who then engage in firt-hand tranaction with a number of ervice provider and then publih their authoritative rating. Other uer then ue thee rating a a bai for forming their own aement of omeone trutworthine. Example of pecialit-baed recommendation are movie and retaurant critic, credit-

8 rating agencie (Moody ) and e-commerce profeional rating agencie, uch a Gomez Advior, Inc. ( The bigget advantage of pecialit-baed recommendation ytem i that it addree the problem of fale rating mentioned above. In mot cae pecialit are profeional and take great pain to build and maintain their trutworthine a diintereted, fair ource of opinion (otherwie they will quickly find themelve out of buine). On the other hand, pecialit-baed recommendation ytem have a number of hortcoming, which become even more evere in online communitie: Firt, pecialit can only tet a relatively mall number of ervice provider. There i time and cot involved in performing thee tet and, the larger and the more volatile the population of one community, the lower the percentage of certified provider. Second, pecialit mut be able to uccefully conceal their identity or ele there i a danger that provider will provide atypically good ervice to the pecialit for the purpoe of receiving good rating. Third, pecialit are individual with their own tate and internal rating cale, which do not necearily match that of any other uer of the ytem. Individual uer of pecialit rating till need to be able to gauge a pecialit recommendation, in order to derive their own likely aement. Lat but not leat, pecialit typically bae their rating on a very mall number of ample interaction with the ervice provider (often jut one). Thi make pecialit rating a very weak bai from which to etimate the variability of omeone ervice attribute, which i an important apect of omeone trutworthine, epecially in dynamic, time-varying environment. 2.4 Collaborative filtering ytem Collaborative filtering technique (Goldberg et. al., 1992; Renick et. al., 1994; Shardanand and Mae, 1995; Billu and Pazzani, 1998) attempt to proce raw rating contained in a recommendation repoitory in order to help rater focu their attention only on a ubet of thoe rating, which are mot likely to be ueful to them. The baic idea behind collaborative filtering i to ue pat rating ubmitted by an agent b a a bai for locating other agent b, b, whoe rating are likely to be mot ueful to agent b in order to accurately predict omeone reputation from it own ubjective perpective. There are everal clae of propoed technique: Claification or clutering approache rely on the aumption that agent communitie form a relatively mall et of tate cluter, with the property that rating of agent of the ame cluter for imilar thing are imilar to each other. Therefore, if the tate cluter of an agent b can be identified, then rating of other member of that cluter for an attribute Q of agent can be ued a tatitical ample for calculating the etimated rating Rˆ for that ame attribute from the perpective of b. b

9 The problem of identifying the right tate cluter for a given agent reduce to the well-tudied problem of claification/data clutering (Kaufman and Roueeuw, 1990; Jain et, al. 1999; Gordon, 1999). In the context of collaborative filtering, the imilarity of two buyer i a function of the ditance of their rating for commonly rated eller. Collaborative filtering reearcher have experimented with a variety of approache, baed on tatitical imilarity meaure (Renick et. al., 1994; Breee et. al., 1998) a well a machine learning technique (Billu and Pazzani, 1998). Regreion approache rely on the aumption that the rating of an agent b can often be related to the rating of another agent b through a linear relationhip of the form j i R bi = β for all agent (1) α R + ij b j ij Thi aumption i motivated by the belief, widely accepted by economit (Arrow, 1963; Sen, 1986) that, even when agent have imilar tate, one uer internal cale i not comparable to another uer cale. According to thi belief, in a given community the number of trict nearet neighbor will be very limited while the aumption of (1) open the poibility of uing the recommendation of a much larger number of agent a the bai for calculating an agent reputation. In that cae, if we can etimate the parameter α, for each pair of agent, we can ue formula (1) to tranlate the rating of agent b to the ij β ij internal cale of agent b and then treat the tranlated rating a tatitical ample for etimating the reputation Rˆ b i i from the perpective of agent b. The problem of etimating thoe parameter reduce to the i well-tudied problem of linear regreion. There i a huge literature on the topic and a lot of efficient technique, which are applicable to thi context (Malinvaud, 1966; Pindyck and Rubinfeld, 1981). j Both claification and regreion approache relate buyer to one another baed on their rating for a common et of eller. If the univere of eller i large enough, even active buyer may have rated a very mall ubet of eller. Accordingly, claification and regreion approache may be unable to calculate etimated reputation for many eller-buyer pair. Furthermore, the accuracy of uch reputation etimate may be poor becaue fairly little rating data can be ued to derive them. Thi problem i known a reduced coverage and i due to the pare nature of rating. Such weaknee are prompting reearcher to experiment with the ue of technique from the field of Knowledge Dicovery in Databae (Fayyad et. al. 1996), which dicover latent relationhip among element of pare databae in the context of online reputation reporting ytem. The promiing ue of one uch technique, Singular Value Decompoition (SVD), ha been reported in (Billu and Bazzani 1998; Sarwar et. al. 2000). 3. The effect of unfair rating in online reputation reporting ytem

10 Of the variou clae of ytem urveyed in the previou ection, we believe that recommendation repoitorie with collaborative filtering have the bet potential for calability and accuracy. Neverthele, while thee technique addre iue related to the ubjective nature of rating, they do not addre the problem of unfair rating. Thi ection look at thi problem in more detail. More pecifically, our goal i to tudy a number of unfair rating cenario and analyze their effect in compromiing the reliability of a collaborative-filtering-baed reputation reporting ytem. To implify the dicuion, in the ret of the paper we are making the following aumption: We aume a trading community whoe participant are ditinguihed into buyer and eller. We further aume that only buyer can rate eller. In a future tudy we will conider the implication of bi-directional rating. In a typical tranaction t, a buyer b contract with a eller for the proviion of a ervice. Upon concluion of the tranaction, b provide a numerical rating, reflecting ome attribute Q of the ervice offered by a perceived by b (rating can only be ubmitted in conjunction with a tranaction). Again, for the ake of implicity we aume that critical attribute and (t) R b (t) R b i a calar quantity, although, in mot tranaction there are more than one would be a vector. (t) R b We further aume the exitence of an online reputation reporting mechanim, whoe goal i to tore and proce pat rating in order to calculate reliable peronalized reputation etimate ( t) for eller upon requet of a propective buyer b. In etting where the critical attribute Q for which rating are provided i ubjectively meaurable, there exit four cenario where buyer and/or eller can intentionally try to rig the ytem, reulting in biaed reputation etimate, which deviate from a fair aement of attribute Q for a given eller: ˆ R b a. Unfair rating by buyer Unfairly high rating ( ballot tuffing ): A eller collude with a group of buyer in order to be given unfairly high rating by them. Thi will have the effect of inflating a eller reputation, therefore allowing that eller to receive more order from buyer and at a higher price than he deerve. Unfairly low rating ( bad-mouthing ): Seller can collude with buyer in order to bad-mouth other eller that they want to drive out of the market. In uch a ituation, the conpiring buyer provide unfairly negative rating to the targeted eller, thu lowering their reputation. b. Dicriminatory eller behavior Negative dicrimination: Seller provide good ervice to everyone except a few pecific buyer that they don t like. If the number of buyer being dicriminated upon i relatively mall, the cumulative reputation of eller will be good and an externality will be created againt the victimized buyer.

11 Poitive dicrimination: Seller provide exceptionally good ervice to a few elect individual and average ervice to the ret. The effect of thi i equivalent to ballot tuffing. That i, if the favored group i ufficiently large, their favorable rating will inflate the reputation of dicriminating eller and will create an externality againt the ret of the buyer. The obervable effect of all four above cenario i that there will be a diperion of rating for a given eller. If the rated attribute i not objectively meaurable, it will be very difficult, or impoible to ditinguih rating diperion due to genuine tate difference from that which i due to unfair rating or dicriminatory behavior. Thi create a moral hazard, which require additional mechanim in order to be either avoided, or detected and reolved. In the following analyi, we aume the ue of collaborative filtering technique in order to addre the iue of ubjective rating. More pecifically, we aume that, in order to etimate the peronalized reputation of from the perpective of b, ome collaborative filtering technique i ued to identify the nearet neighbor et N of b. N include buyer who have previouly rated and who are the nearet neighbor of b, baed on the imilarity of their rating, for other commonly rated eller, with thoe of b. Sometime, thi tep will filter out all unfair buyer. Suppoe, however, that the colluder have taken collaborative filtering into account and have cleverly picked buyer whoe tate are imilar to thoe of b in everything ele except their rating of. In that cae, the reulting et N will include ome fair rater and ome unfair rater. Effect when reputation i teady over time The implet cenario to analyze i one where we can aume that agent behavior, and therefore reputation, remain teady over time. That mean that, collaborative filtering algorithm can take into account all rating in their databae, no matter how old. In order to make our analyi more concrete, we will make the aumption that fair rating can range between [ R, R ] and that they follow a ditribution of the general form: min max τ ( R) = max( R, min( R, z)) where z ~ N ( µ, σ ) (2) b min max which in the ret of the paper will be approximated to τ ( R) N( µ, σ ). The introduction of minimum and maximum rating bound correpond nicely with common practice. The aumption of normally ditributed fair rating, require more dicuion. It i baed on the previou aumption that thoe rating belong to the nearet neighbor et of a given buyer, and therefore repreent a ingle tate cluter. Within a tate cluter, it i expected that fair rating will be relatively cloely clutered around ome value and hence the aumption of normality. b

12 In thi paper we will focu on the reliable etimation of the reputation mean. Given all the above aumption, the goal of a reliable reputation reporting ytem hould be the calculation of a fair mean reputation etimate (MRE) Rˆ b ditribution in the nearet neighbor et. Ideally, therefore: which i equal to or very cloe to µ, the mean of the fair rating

13 ˆ (3) R b, fair = µ On the other hand, the goal of unfair rater i to trategically introduce unfair rating in order to maximize the ditance between the actual MRE ˆ R b, actual calculated by the reputation ytem and the fair MRE. More pecifically the objective of ballot-tuffing agent i to maximize the MRE while bad-mouthing agent aim to minimize it. Note that, in contrat to the cae of fair rating, it i not afe to make any aumption about the form of the ditribution of unfair rating. Therefore, all analye in the ret of thi paper will calculate ytem behavior under the mot diruptive poible unfair rating trategy. We will only analyze the cae of ballot-tuffing ince the cae of bad-mouthing i ymmetrical. Aume that the initial collaborative filtering tep contruct a nearet neighbor et N, in which the proportion of unfair rater i δ and the proportion of fair rater i δ. Finally, our baeline analyi in thi ection aume that the actual MRE ˆ R b, actual i taken to be the ample mean of the mot recent rating given to by each qualifying rater in N. Thi imple etimator i conitent with the practice of mot current-generation commercial recommender ytem (Schafer et. al. 2001). In that cae, the actual MRE will approximate: R ˆ (1 δ ) µ + δ µ b, actual u (4) where µ i the mean value of unfair rating. The trategy, which maximize the above MRE i one where u µ, i.e. where all unfair buyer give the maximum poible rating to the eller. = R u max We define the mean reputation etimate bia for a contaminated et of rating to be: ˆ B = R b, actual ˆ (5) R b, fair In the above cenario, the maximum MRE bia i given by: B = 1 δ ) µ + δ R µ = δ ( R ) (6) ( µ max max max Figure 1 tabulate ome value of B max for everal different value µ and δ, in the pecial cae where rating range from [0,9]. For the purpoe of comparing thi baeline cae with the immunization mechanim decribed in Section 4, we have highlighted biae above 5% of the rating range (i.e. biae greater than ±0.5 point on rating which range from 0-9). A can be een, formula (6) can reult in very ignificant inflation of a eller MRE, epecially for mall µ and large δ. Percentage of unfair rating Fair Mean Reputation Etimate (R min =0, R max =9) Reputation Bia

14 9% % % % % Figure 1. Some value of maximum MRE bia when MRE are baed on the mean of the rating et. Shaded cell indicate biae above 5% of the rating range. Effect when reputation varie over time Thi ection expand our analyi by dicuing ome additional conideration, which arie in environment where eller behavior, and therefore reputation, may vary over time. We identify ome additional unfair rating trategie that can be very diruptive in uch environment. In real-life trading communitie, eller may vary their ervice quality over time, improving it, deteriorating it, or even ocillating between phae of improvement and phae of deterioration. In hi eminal analyi of the economic effect of reputation, (Shapiro 1981) proved that, in uch environment, the mot economically efficient way to etimate a eller reputation (i.e. the way that induce the eller to produce at the highet quality level) i a a time dicounted average of recent rating. Shapiro went even further to prove that efficiency i higher (1) the higher the weight placed on recent quality rating and (2) the higher the dicount factor of older rating. In thi paper we are baing our analyi on an approach, which approximate Shapiro deiderata, but i impler to implement and analyze. The principal idea i to calculate time varying peronalized MRE ˆ R b equivalent to uing a time dicounted average calculation where weight are equal to 1 for rating ubmitted within W and 0 otherwie. More pecifically, in order to calculate a time varying peronalized MRE R u ( t) a average of rating ubmitted within the mot recent time window W=[t-ε, t] only. Thi i ˆR (t), we firt ue collaborative filtering in order to contruct an initial nearet neighbor et N b Following that we contruct the active nearet neighbor et N active, coniting only of thoe buyer u N initial who have ubmitted at leat one rating for within W. Finally, we bae the calculation of (t) where u Nactive and t W. ˆ R b initial. ( t) on rating Formula (6) make it clear that the maximum reputation bia due to unfair rating i proportional to the ratio δ of unfair rating, which make it into the active nearet neighbor et N active. Therefore, an obviou trategy for unfair buyer i to try to increae δ by flooding the ytem with unfair rating. (Zacharia et. al. 1999) touch upon thi iue and propoe keeping only the lat rating given by a given buyer to a given

15 eller a a olution. In environment where reputation etimate ue all available rating, thi imple trategy enure that eventually δ can never be more than the actual fraction of unfair rater in the community, uually a very mall fraction. However, the trategy break down in environment where reputation etimate are baed on rating ubmitted within a relatively hort time window (or where older rating are heavily dicounted). The following paragraph explain why. Let u aume that the initial nearet neighbor et N initial contain m fair rater and n unfair rater. In mot cae n << m. Aume further that the average interarrival time of fair rating for a given eller i λ and that peronalized MRE ˆ R b ( t) are baed only on rating for ubmitted by buyer u Ninitial within the time window W = [t kλ, t]. Baed on the above aumption, the average number of fair rating ubmitted within W would be equal to k. To enure accurate reputation etimate, the width of the time window W hould be relatively mall; therefore k hould generally be a mall number (ay, between 5 and 20). For k << m we can aume that every rating ubmitted within W i from a ditinct fair rater. Aume now that unfair rater flood the ytem with rating at a frequency much higher than the frequency of fair rating. If the unfair rating frequency i high enough, every one of the n unfair rater will have ubmitted at leat one rating within the time window W. A uggeted by Zacharia et. al., we keep only the lat rating ent by each rater. Even uing that rule, however, the above cenario would reult in an active nearet neighbor et of rater where the fraction of unfair rater i δ = n/(n+k). Thi expreion reult in δ 0.5 for n k, independent of how mall n i relative to m. For example, if n=10 and k=5, δ = 10/(10+5) = We therefore ee that, for relatively mall time window, even a mall (e.g. 5-10) number of colluding buyer can uccefully ue unfair rating flooding to dominate the et of rating ued to calculate MRE and completely bia the etimate provided by the ytem. The reult of thi ection indicate that even a relatively mall number of unfair rater can ignificantly compromie the reliability of collaborative-filtering-baed reputation reporting ytem. Thi require the development of effective meaure for addreing the problem. Next ection propoe and analyze everal uch meaure. 4. Mechanim for immunizing online reputation reporting ytem againt unfair rater behavior Having recognized the problem of unfair rating a a real and important one, thi ection propoe a number of mechanim for eliminating or ignificantly reducing it advere effect on the reliability of online reputation reporting ytem. 4.1 Avoiding negative unfair rating uing controlled anonymity The main argument of thi ection i that the anonymity regime of an online community can influence the kind of reputation ytem attack that are poible. A lightly urpriing reult i the realization that a fully

16 tranparent marketplace, where everybody know everybody ele true identity incur more danger of reputation ytem fraud than a marketplace where the true identitie of trader are carefully concealed from each other but are known to a truted third entity (uually the market-maker). Bad-mouthing and negative dicrimination are baed on the ability to pick a few pecific victim and give them unfairly poor rating or provide them with poor ervice repectively. Uually, victim are elected baed on ome real-life attribute of their aociated principal entitie (for example, becaue they are our competitor or becaue of religiou or racial prejudice). Thi advere election proce can be avoided if the community conceal the true identitie of the buyer and eller from each other. In uch a controlled anonymity cheme, the marketplace know the true identity of all market participant by applying ome effective authentication proce before it allow acce to any agent (Hutt et. al. 1995). In addition, it keep track of all tranaction and rating. The marketplace publihe the etimated reputation of buyer and eller but keep their identitie concealed from each other (or aign them peudonym which change from one tranaction to the next, in order to make identity detection very difficult). In that way, buyer and eller make their deciion olely baed on the offered term of trade a well a the publihed reputation. Becaue they can no longer identify their victim, bad-mouthing and negative dicrimination can be avoided. It i intereting to oberve that, while, in mot cae, the anonymity of online communitie ha been viewed a a ource of additional rik (Kollock 1999; Friedman and Renick 1999), here we have an example of a ituation where ome controlled degree of anonymity can be ued to eliminate ome tranaction rik. Concealing the identitie of buyer and eller i not poible in all domain. For example, concealing the identity of eller i not poible in retaurant and hotel rating (although concealing the identity of buyer i). In other domain, it may require the creative intervention of the marketplace. For example, in a marketplace of electronic component ditributor, it may require the marketplace to act a an intermediary hipping hub that will help erae information about the eller addre. If concealing the identitie of both partie from each other i not poible, then it may till be ueful to conceal the identity of one party only. More pecifically, concealing the identity of buyer but not eller avoid negative dicrimination againt hand picked buyer but doe not avoid bad-mouthing of hand picked eller. In an analogou manner, concealing the identity of eller but not buyer avoid bad-mouthing but not negative dicrimination. Thee reult are ummarized in Figure 2. Generally peaking, concealing the identitie of buyer i uually eaier than concealing the identitie of eller (a imilar point i made in Cranor and Renick 1999). Thi mean that negative dicrimination i eaier to avoid than bad-mouthing. Furthermore, concealing the identitie of eller before a ervice i performed i uually eaier than afterward. In domain with thi property, controlled anonymity can be ued at the eller election tage in order to, at leat, protect eller from being intentionally picked for

17 ubequent bad-mouthing. For example, in the above-mentioned marketplace of electronic component ditributor, one could conceal the identitie of eller until after the cloing of a deal. Auming that the number of ditributor for a given component type i relatively large, thi trategy would make it difficult, or impoible, for malevolent buyer to intentionally pick pecific ditributor for ubequent badmouthing. Anonymity Regime Buyer identity known to eller Seller identity known to buyer Bad-mouthing poible Clae of poible unfair behavior Negative dicrimination poible Ballot-tuffing poible Poitive dicrimination poible Ye Ye b b b b Ye No b b b No Ye b b b No No b b Figure 2. Effect of controlled anonymity in preventing certain clae of unfair behavior. It i important to note at thi point that even when identitie of buyer and eller are concealed, buyer and eller who have an incentive to ignal their identitie to each other can alway find clever way to do o. For example, eller involved in a ballot tuffing cheme can ue a particular pattern in the amount that they bid (e.g. amount ending in.33) in order to ignal their preence to their conpirator. Therefore, while controlled anonymity can avoid bad-mouthing and negative dicrimination, it cannot avoid ballot tuffing and poitive dicrimination. The following two ection propoe ome filtering mechanim, which are applicable in the cae of ballot tuffing a well. 4.2 Reducing the effect of unfair rating uing median filtering In Section 3 we have baed our calculation of reputation bia on the aumption that MRE are baed on the ample mean of the nearet neighbor et. In thi ection we will demontrate that the effect of unfair rating can be ignificantly reduced if, intead of the ample mean, the calculation of MRE i baed on the ample median. The field of robut tatitic ha devoted coniderable attention to the problem of finding etimator of location (mean value), which are robut in the preence of contaminated ample (Huber, 1981). Neverthele, mot of that literature treat contamination a innocent noie and doe not addre the problem of maliciou rater who, baed on their knowledge of the etimator ued, trategically ditribute unfair rating in order to maximize the achievable bia. To the knowledge of the author, the analyi preented in thi ection i novel.

18 The ample median Y ~ of n ordered obervation Y Y... Y i the middle obervation Y where 1 2 n k k= (n+1)/2 if n i odd. When n i even then Y ~ i conidered to be any value between the two middle obervation Y and Y k k + 1 where k=n/2, although it i mot often taken to be their average. In the abence of unfair rating (i.e. when δ=0) we have previouly aumed that ( R) N( µ, σ ). It i well known (Hojo, 1931) that, a the ize n of the ample increae, the median of a ample drawn from a normal ditribution converge rapidly to a normal ditribution with mean equal to the median of the parent ditribution. In normal ditribution, the median i equal to the mean. Therefore, in ituation where there are no unfair rater, the ue of the ample median reult in unbiaed fair MRE: ˆ (7) R b, fair µ Let u now aume that unfair rater know that MRE are baed on the ample median. They will trategically try to introduce unfair rating whoe value will maximize the abolute bia between the ample median of the fair et and the ample median of the contaminated et. More pecifically, ballot tuffer will try to maximize that bia while bad-mouther will try to minimize it. In the following analyi we conider the cae of ballot tuffing. The cae of bad-mouthing i ymmetric, with the ign revered. Auming that the nearet neighbor et conit of n f τ = ( 1 δ ) n fair rating and n = δ n unfair rating, where 0 δ < 0. 5, the mot diruptive unfair rating trategy, in term of influencing the ample median, i one where all unfair rating are higher than the ample median of the contaminated et. In that cae and for δ < 0. 5, all the rating, which are lower than or equal to the ample median will have to be fair rating. Then, the ample median of the contaminated et, will be identical to the k th order tatitic of the et of n fair rating, where k=(n+1)/2. f b u It ha been hown (Cadwell 1952) that, a the ize n of the ample increae, the k th order tatitic of a ample drawn from a normal ditribution N( µ, σ ) converge rapidly to a normal ditribution with mean equal to the q th quantile of the parent ditribution where q=k/n. Therefore, for large rating ample n, under the wort poible unfair rating trategy, the ample median of the contaminated et will converge to x q where x q i defined by: σ Pr[ R x ] = q x = Φ 1 ( q) + µ (8) b q q

19 k n + 1 n where q = = = 2 2 (1 δ ) (9) n n n n 2 (1 δ ) f f and Φ 1 ( q) i the invere tandard normal CDF. Given that ˆ R b, fair µ the aymptotic formula for the average reputation bia achievable by δ 100% unfair rating when fair rating are drawn from a normal ditribution N( µ, σ ) and unfair rating follow the mot diruptive poible unfair rating ditribution, i given by: ˆ ˆ 1 1 E [ B ] = E[ R R ] = σ Φ ( ) (10) max b, actual b, fair 2 (1 δ ) Figure 3 how ome of the value of E[ B ] for variou value of max δ and σ in the pecial cae where rating range from 0 to 9. Given that we have aumed that all rating in the nearet neighbor et correpond to uer in the ame tate cluter, it i expected that the tandard deviation of the fair rating will be relatively mall. Therefore, we did not conider tandard deviation higher than 10% of the rating range. It i obviou that the maximum bia increae with the percentage of unfair rating and i directly proportional to the tandard deviation of the fair rating. A before, we have highlighted maximum average biae of 5% of the rating range or more. Figure 3 clearly how that the ue of the ample median a a the bai of calculating MRE manage to reduce the maximum average bia to below 5% of the rating range for unfair rater ratio of up to 30-40% and a wide range of fair rating tandard deviation. Percentage of unfair rating Standard Deviation of Fair Rating Reputation Bia 9% % % % % Figure 3. Aymptotic upper bound of average reputation bia when MRE are baed on the median of the rating et (rating range from 0-9). In mot collaborative filtering context, nearet neighbor reputation etimate are baed on ample with relatively mall ize, typically 5-15 rating. Given that the above theoretical reult are aymptotic, or large ample reult, it i important to invetigate how well they hold in the cae of mall ample ize. To find that out, we have performed imulation experiment for ample ize n=5 and n=11. The experiment reulted in remarkable correpondence between theory and practice. Detail of the experimental reult are reported in (Dellaroca 2000).

20 4.3 Uing frequency filtering to eliminate unfair rating flooding Formula (6) and (10) confirm the intuitive fact that the reputation bia due to unfair rating increae with the ratio δ of unfair rater in a given ample. In etting where a eller quality attribute can vary over time (mot realitic etting), calculation of reputation hould be baed on recent rating only uing time dicounting or a time-window approach. In thoe cae, Section 3 demontrated that by flooding the ytem with rating, a relatively mall number of unfair rater can manage to increae the ratio δ of unfair rating in any given time window above 50% and completely compromie the reliability of the ytem. Thi ection propoe an approach for effectively immunizing a reputation reporting ytem againt unfair rating flooding. The main idea i to filter rater in the nearet neighbor et baed on their rating ubmiion frequency. Decription of frequency filtering Step 1: Frequency filtering depend on etimating the average frequency of rating ubmitted by each buyer for a given eller. Since thi frequency i a time-varying quantity (eller can become more or le popular with the paage of time), it, too need to be etimated uing a time window approach. More pecifically: 1. Calculate the et F (t) of buyer-pecific average rating ubmiion frequencie f (t) b for eller, for each buyer b that ha ubmitted rating for during the rating ubmiion frequency calculation time window W =[t-e, t]. More preciely, f f (t) b = (number of rating ubmitted for by b during W )/E (11) f 2. Set the cutoff frequency f (t) cutoff to be equal to the k-th order tatitic of the et F (t) where k = ( 1 -D) n, n i the number of element of F (t) and D i a conervative etimate of the fraction of unfair rater in the total buyer population for eller. For example, if we aume that there are no more than 10% unfair rater among all the buyer for eller, then D=0.1. Auming further that n=100, i.e. that the et F (t) contain average rating ubmiion frequencie from 100 buyer, then the cutoff frequency would be equal to the 90-th mallet frequency (the 10-th larget frequency) preent in the et F (t). The width E of the rating ubmiion frequency calculation time window W hould be large enough in order to contain at leat a few rating from all buyer for a given eller. f

21 Step 2: During the calculation of a MRE for eller, eliminate all rater b in the nearet neighbor et for whom f > f. In other word, eliminate all buyer whoe average rating ubmiion frequency for b cutoff eller i above the cutoff frequency. Analyi of frequency filtering We will how that frequency filtering provide effective protection againt unfair rating flooding by guaranteeing that the ratio of unfair rater in the MRE calculation et cannot be more than twice a large a the ratio of unfair rater in the total buyer population. A before, we will aume that the entire buyer population i n, unfair rater are δ n << n and the width of the reputation etimation time window i a relatively mall W (o that, each rating within W typically come from a different rater). Then, after applying frequency filtering to the nearet neighbor et of rater, in a typical time window we expect to find f cutoff W ( 1 δ ) n u ϕ( u) du fair rating, whereϕ(u) i the probability denity function of fair rating frequencie, and at mot W n α f cutoff δ unfair rating, where 0 α 1 i the fraction of unfair rater with ubmiion frequencie below f cutoff. Therefore, the unfair/fair rating ratio in the final et would be equal to: unfair rating fair rating δ ' δ = = 1 δ ' 1 δ α f cutoff δ = I 1 δ u ϕ( u) du f cutoff (12) where I = α f f cutoff cutoff u ϕ( u) du denote the inflation of the unfair/fair rating ratio in the final et relative to it value in the original et. The goal of unfair rater i to trategically ditribute their rating frequencie above and below the cutoff frequency in order to maximize I. In contrat, the goal of the market deigner i to pick the cutoff frequency f cutoff o a to minimize I. The cutoff frequency ha been defined a the (1-D) n-th order tatitic of the ample of buyer frequencie, where D δ. For relatively large ample, thi converge to the q-th quantile of the fair rating frequencie ditribution, where q atifie the equation: