EVALUATION of REPUTATION METRIC for the B2C E-COMMERCE REPUTATION SYSTEM

EVALUATION OF REPUTATION METRIC FOR THE B2C e-COMMERCE REPUTATION SYSTEM Anna Gutowska and Andrew Sloane School of Computing and Information Technology, University of Wolverhampton Wulfruna Street, Wolverhampton, U.K. Keywords: Reputation System, B2C, e-Commerce, Simulation. Abstract: This paper evaluates recently developed novel and comprehensive reputation metric designed for the distributed multi-agent reputation system for the Business-to-Consumer (B2C) E-commerce applications. To do that an agent-based simulation framework was implemented which models different types of behaviours in the marketplace. The trustworthiness of different types of providers is investigated to establish whether the simulation models behaviour of B2C E-commerce systems as they are expected to behave in real life. 1 INTRODUCTION each other to derive a trust or reputation rating. This can assist in deciding whether or not to engage in a The process of globalization creates new challenges transaction with this party. Reputation systems are and opportunities for companies by offering an particularly useful in cases where the trustee is access to new markets that were previously closed unknown to the individual involved but well known due to cost, regulations etc. The adoption of the to others. Internet, in particular Internet-enabled B2C E- There are a number of existing consumer-to- business solutions, allows many Small and Medium consumer (C2C) on-line reputation systems such as Enterprises (SMEs) to respond to these challenges eBay (2008) or Amazon (2008). However, unlike C2C E-commerce marketplaces, most B2B sites do and opportunities by extending the geographic reach not provide users with feedback information. There of their operations. Very often however, Websites are some centralized services/websites though, created for sales purposes are simple in design and which offer store ratings and reviews to their users, functionality and therefore, do not arouse trust at such as BizRate (2008) or Resellerratings (2008). first glance. Furthermore, in contrast to “big brands” All of them however, rely only on simple algorithms which have already established their reputation in calculating the average rating based on the given the online marketplaces, SMEs are unknown to feedback. many E-commerce customers. Nevertheless, much academic work on reputation In the E-commerce environment, which does not systems has been devoted to the C2C part of E- require the physical presence of the participants, commerce (Peer-to-Peer networks) which can be there is a high level of “uncertainty” regarding the found reviewed in (Sabater and Sierra, 2005; Josang reliability of the services, products, or providers. et al., 2007; Gutowska and Bechkoum, 2007a; Marti Although many technologies exist to make the and Garcia-Molina, 2006). Unlike the existing transaction more secure, there is still the risk that the centralized approaches (e.g. eBay, Amazon) which unknown provider will not comply with the protocol are single-factor based, many authors proposed used. Thus, the decision of who to trust and with distributed reputation systems which still tend to be whom to engage in a transaction becomes more “one issue-centric” (Lin et al., 2005; Bamasak and difficult and falls on the shoulders of the Zhang, 2005; Huynh et al., 2006; Fan et al., 2005) individuals. In such an environment, reputation (addressing only one of many problems existing in systems come in place to assist consumers in the reputation systems (Josang et al., 2007; decision making. The basic idea is to let parties rate Gutowska and Bechkoum, 2007a; Marti and Garcia- 489 Gutowska A. and Sloane A. EVALUATION OF REPUTATION METRIC FOR THE B2C e-COMMERCE REPUTATION SYSTEM. DOI: 10.5220/0001831104890498 In Proceedings of the Fifth International Conference on Web Information Systems and Technologies (WEBIST 2009), page ISBN: 978-989-8111-81-4 Copyright c 2009 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved WEBIST 2009 - 5th International Conference on Web Information Systems and Technologies Molina, 2006)). Even in studies attempting to 2 REPUTATION SYSTEM provide more complex reputation methods, for FRAMEWORK example work on Histos/Sporas (Zacharia et al., 2000), some issues are still not taken into consideration, such as the transaction value, age of The simulated reputation metric in this study is rating, or credibility of referees. designed for the B2C E-commerce reputation system Many of the problems addressed in C2C in which two main roles are considered: buyer agent reputation models also apply to the B2C E- i.e. agent representing a user and provider (web commerce environment. Not many authors however, service). A user agent collects for its user the concentrate on the latter model of the marketplace. distributed reputation ratings about a web service The only work known to the authors addressing it is (provider). In return, a user provides the agent with (Cho et al.) and (Ekstrom and Bjornsson, 2002). ratings about a transaction in order to build the Nevertheless, to the best of the authors’ knowledge, reputation database of the services. Agents create a there are no studies whose main focus is to derive network where they exchange transaction ratings reputation ratings in B2C E-commerce environment about web services their users have dealt with, this taking into account the characteristics of the is called a buyers’ coalition in the paper. In this way providers. they are involved in a joint recommendation This paper aims to evaluate a novel reputation process. metric for computing reputation in a multi-agent User agents and providers are engaged in a distributed B2C E-commerce system. To do that, the transaction process e.g. buying-selling, where agent-based simulation framework was money and products/services are involved. implemented. The strength of the metric is measured To assess the reputation of a provider, first, a by how well it reflects the agents (providers) user agent will use the information from the direct behaviour including resistance against different interactions it has had with that party and second, hostile agents. Other important aspects of reputation the ratings provided by other agents (indirect systems such as privacy of transaction data, ratings) from the buyers’ coalition which have dealt protection against collaboration attacks, and unfair with the provider. ratings are out of the scope of this work. The proposed reputation system is distributed The proposed reputation metric evaluated in this where each user agent will store their opinion about paper offers a comprehensive approach by including transactions with other parties. age of rating, transaction value, credibility of The assumption is that it is in users’ interest to referees, and number of malicious incidents. leave feedback after each transaction as that is the Furthermore, in addition to the information about only way the reputation system will work. The past behaviour it also incorporates other aspects participants are aware that if they want to calculate affecting online trust which are based on providers’ the reputation rating about a particular provider the characteristics. Past behaviour, is not the only only source of information will be their feedback information source affecting trust/reputation rating from the past and feedback received from other of an online vendor. According to previous research users. Also, when entering the system as a new (Gutowska, 2007; Gutowska and Bechkoum, member they are duty-bound to both. The users rate 2007b), there are many issues influencing online transactions they were involved in and share this trust-based decisions such as existence of trustmark information with others when requested. seals, payment intermediaries, privacy statements, security/privacy strategies, purchase protection/insurance, alternative dispute resolutions 3 REPUTATION METRIC as well as existence of first party information. The extended approach evaluated in this paper yields a The reputation metric (Gutowska and Buckley, promising improved distributed B2C reputation 2008a) evaluated in this paper is based on the mechanism. The problem of complexity of the weighted average. The reputation value of provider reputation metric is further described in (Gutowska p is calculated as the arithmetic mean of the and Buckley, 2008a; Gutowska et al., to appear). compulsory reputation (Section 3.1) and the optional reputation (Section 3.2). In addition the weight wm(p) based on the number of malicious incidents is applied. 490 EVALUATION OF REPUTATION METRIC FOR THE B2C e-COMMERCE REPUTATION SYSTEM If optional reputation metric is not calculated Source of Feedback. The reputation metric in this then the reputation metric takes the value of study applies the weight ws(p) based on the rating compulsory reputation metric multiplied by wm(p). tendency concept proposed in (Cho et al.). It Further, the full rating scale of trust is [0; 1]. decreases the rating from the rater who has a tendency to rate higher than others, and vice versa. 3.1 Compulsory Reputation −−= gguws uAu )( )(1)( (1) Compulsory reputation is based on the set of the Where: compulsory parameters and is defined as the arithmetic mean of aggregated direct and indirect gu is the average transaction ratings from a rater u ratings (see below). The rating scale for compulsory gA(u) is the average ratings of the other users from reputation metric is [0; 1]. the subset of the “best/most suitable users” (for the providers that the rater u rated). 3.1.1 Compulsory Parameters Reputation Lifetime. In order to model the Transaction Ratings. In the proposed system the dynamic nature of reputation, the weight associated quality factors constitute the explicit ratings left by with the reputation lifetime wt is applied which the user after the transaction and consist of three constitutes an exponential function of time. In this components (as vector values): transaction outcome way the more recent ratings are considered more i.e. if the product/service was received, fulfilling important and are valued higher comparing to the provider’s signals (Rein, 2005) e.g.

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