CCCI metrics for the measurement of quality of e-service

The growing development in web-based trust and reputation systems in the 21st century will have powerful social and economic impact on all business entities, and will make transparent quality assessment and customer assurance realities in the distributed web-based service oriented environments. The growth in web-based trust and reputation systems will be the foundation for web intelligence in the future. Trust and Reputation systems help capture business intelligence through establishing customer relationships, learning consumer behaviour, capturing market reaction on products and services, disseminating customer feedback, buyers? opinions and end-user recommendations, and revealing dishonest services, unfair trading, biased assessment, discriminatory actions, fraudulent behaviours, and un-true advertising. The continuing development of these technologies will help in the improvement of professional business behaviour, sales, reputation of sellers, providers, products and services. In this paper, we present a new methodology known as CCCI (Correlation, Commitment, Clarity, and Influence) for trustworthiness measure that is used in the Trust and Reputation System. The methodology is based on determining the correlation between the originally committed services and the services actually delivered by a Trusted Agent in a business interaction over the service oriented networks to determine the trustworthiness of the Trusted Agent

Analyzing communities vs. single agent-based Web services: Trust perspectives

Gathering functionally similar agent-based Web services into communities has been proposed and promoted on many occasions. In this paper, we compare the performance of these communities with self-managed, single agent-based Web services from trust perspective. To this end, we deploy a reputation model that ranks communities and Web services with respect to different reputation parameters. By relating the parameters, we extend our discussion to analyze the beneficial cases and incentives for a single Web service to join a community even if this joining could negatively impact other parameters. Besides theoretical discussions of this analysis, we discuss the system implementation along with simulations that depict diverse parameters and system performance. © 2010 IEEE

Scheduling Reputation Maintenance in Agent-based Communities Using Game Theory

In agent-based systems, agents can be organized within groups, called communities, where mem-bers are providing similar or complementary services. An example of such systems is agent-based communities of web services, where web services are abstracted as rational agents and empowered with decision making capabilities and can interact with each other. Managing reputation of each agent and of the whole community is a key issue towards securing this type of systems, where a con-troller agent is designed to observe and check the behavior of each member to update and maintain the system’s reputation. Scheduling the check (i.e. maintenance) by deciding about the moments where the check has to be done is still an open problem. Because it is highly expensive, maintenance cannot be done every moment or based on small history of agents’ behaviors. We propose in this thesis a scheduling algorithm that helps the controller agent improve the quality of the reputation mechanism, which increases the trust value of users toward the community. The proposed algorithm is based on a class of games called Bayesian Stackelberg. Our Bayesian Stackelberg game is designed between the controller agent and community members. We simulate and compare the efficiency of our algorithm with other stochastic techniques, namely uniform, normal and Poisson distributions. This research draws the lines for future work in the subject of optimizing reputation mechanisms through maintenance in different time intervals

Efficient Approach Towards an Agent-Based Dynamic Web Service Discovery Framework with QoS Support

Abstract. Web services are about the integration of applications via the Web. Hereby, the programming effort should be minimized through the reuse of standardized components and interfaces. One of the fundamental pillars of the Web service vision is a brokerage system that enables services to be published to a searchable repository and later retrieved by potential users. One of the subtasks in a service-oriented architecture is service discovery. Service discovery, the identification of existing Web Services that can be used by new Web applications, is one of the most critical problems deterring Web Service (WS) technology. Current solution is based on UDDI catalogue browsing that supports only primitive matching mechanisms and provides no control over the quality of registered services Quality of Service (QoS) is becoming an important criterion for selection of the best available service. Currently the problem is twofold. The Universal Description, Discovery and Integration (UDDI) registries do not have the ability to publish the QoS information, and the authenticity of the advertised QoS information available elsewhere may be questionable. We aim to refine the discovery process through designing a new framework that enhances retrieval algorithms by combining syntactic and semantic matching of services with QoS. We propose a model of QoS-based Web services discovery that combines an augmented UDDI registry to publish the QoS information and a reputation manager to assign reputation scores to the services based on customer feedback of their performance. The Certifier verifies the QoS claims from the Web service suppliers. A discovery agent facilitates QoS-based service discovery using the reputation scores in a service matching, ranking and selection algorithm. The novelty of our model lies in its simplicity and in its coordination of the above mentioned components. The Proposed framework should give Web services consumers some confidence about the quality of services of the discovered Web services

Incentive-based reputation of WEB services communities

There have been always motivations to introduce clustering of entities with similar functionality into groups of redundant services or agents. Communities of Web services are composed by aggregating a number of functionally identical Web services. Many communities with the same type of service can be formed and all aim to increase their reputation level in order to obtain more requests. The problem, however, is that there are no incentives for these communities to act truthfully and not providing fake feedback in support of themselves or against others. In this thesis we propose an incentive and game-theoretic-based mechanism dealing with reputation assessment for communities of Web services. The proposed reputation mechanism is based on after-service feedback provided by the users to a logging system. Given that the communities are free to decide about their actions, the proposed method defines the evaluation metrics involved in reputation assessment of the communities and supervises the logging system by means of controller agent in order to verify the validity and soundness of the feedback. We also define incentives so that the best game-theoretical strategy for communities is to act truthfully. Theoretical analysis of the framework along with empirical results are provided

Simulating the conflict between reputation and profitability for online rating portals

We simulate the process of possible interactions between a set of competitive services and a set of portals that provide online rating for these services. We argue that to have a profitable business, these portals are forced to have subscribed services that are rated by the portals. To satisfy the subscribing services, we make the assumption that the portals improve the rating of a given service by one unit per transaction that involves payment. In this study we follow the 'what-if' methodology, analysing strategies that a service may choose from to select the best portal for it to subscribe to, and strategies for a portal to accept the subscription such that its reputation loss, in terms of the integrity of its ratings, is minimised. We observe that the behaviour of the simulated agents in accordance to our model is quite natural from the real-would perspective. One conclusion from the simulations is that under reasonable conditions, if most of the services and rating portals in a given industry do not accept a subscription policy similar to the one indicated above, they will lose, respectively, their ratings and reputations, and, moreover the rating portals will have problems in making a profit. Our prediction is that the modern portal-rating based economy sector will eventually evolve into a subscription process similar to the one we suggest in this study, as an alternative to a business model based purely on advertising

Delivering services by building and running virtual organisations

Non peer reviewedPostprin

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

Trust Strategies for the Semantic Web

Everyone agrees on the importance of enabling trust on the SemanticWebto ensure more efficient agent interaction. Current research on trust seems to focus on developing computational models, semantic representations, inference techniques, etc. However, little attention has been given to the plausible trust strategies or tactics that an agent can follow when interacting with other agents on the Semantic Web. In this paper we identify five most common strategies of trust and discuss their envisaged costs and benefits. The aim is to provide some guidelines to help system developers appreciate the risks and gains involved with each trust strategy