Reputation measure approach of web service for service selection

Wang, Shangguang; Sun, Qibo; Zou, Hua; Yang, Fangchun

doi:10.1049/iet-sen.2010.0077

Cited by 23 publications

(49 citation statements)

References 24 publications

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“…Maximilien E M et al formally define service quality attributes by ontology, and propose a framework for dynamic web services selection based on a three-layer ontology structure [10,11,12]. Generally, same quality attributes may have different importance for different web service users in measuring service satisfaction [6,13]. However, these solutions do not take into account user credibility which is very important to assure the accuracy of predicting service reputation.…”

Section: Related Workmentioning

confidence: 99%

User-credibility Based Service Reputation Management for Service Selection

Cao¹,

Dong²,

Qin³

et al. 2014

Proceedings of the 3rd International Conference on Computer Science and Service System

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Abstract-In composite services, the atomic service reputation is becoming important when many similar functional services could be provided for selecting under the heterogeneous and loose-coupled circumstance. However, existing reputation measurement methods pay little attention on user credibility which has a great influence on accuracy. In this paper, we define user credibility as the ability of honest users to provide rational feedbacks, and propose a novel service reputation management model based on service invocation information and user feedback. This method consists of three parts: malicious feedbacks distinguishing, user credibility evaluating and service reputation predicting. Firstly, a clustering algorithm is conceived to filter out malicious feedbacks. Then a feedback deviation based algorithm is proposed to evaluate the user credibility considering service QoS similarity. Finally, an algorithm on the basis of user credibility and honest feedbacks is applied to predict service reputation. Experiment proves that our model can effectively detect malicious feedbacks and precisely measure service reputation with low error.

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Section: Related Workmentioning

confidence: 99%

User-credibility Based Service Reputation Management for Service Selection

Cao¹,

Dong²,

Qin³

et al. 2014

Proceedings of the 3rd International Conference on Computer Science and Service System

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show abstract

“…The reputation attribute of QoS is investigated and considered in [6]. It is very important for users to obtain reliable services in service selection.…”

Section: Services Selectionmentioning

confidence: 99%

An adaptively emerging mechanism for context-aware service selections regulated by feedback distributions

Zhu

Jin

2012

Hum. Cent. Comput. Inf. Sci.

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Background: In the cloud computing environments, numerous ambient services may be created speedily and provided to a variety of users. In such a situation, people may be annoyed by how to make a proper and optimal selection quickly and economically. Methods: In this study, we propose an Adaptively Emerging Mechanism (AEM) to reduce this selection burden with an interdisciplinary approach. AEM is applied and integrated into the Flowable Service Model (FSM), which has been proposed and developed in our previous study. We consider the user's feedback information is a pivotal factor for AEM, which contains the user's satisfaction degree after using the services. At the same time, we assume that these factors, such as the service cost, matching result precision, responding time, personal and social context information, etc., are essential parts of the optimizing process for the selection of ambient services. Results and Conclusion: By analyzing the result of AEM simulation, we reveal that AEM can (1) substantially improve the selection process for LOW feedback users; (2) bring no negative effect on the selection process for MEDIUM or HIGH feedback users; and (3) enhance the rationality for services selection.

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“…Finally, by taking into account the defined weights as well as adjusting the referred values according to degree of trust to the provider, the consumer agent is able to generate his degree of trust in a service provider. The work in [22], in opposition to feedback rating-based approaches that introduce subjectivity, proposes a reputation management method for selecting web services in three steps: testing, identifying malevolent, and tuning feedbacks. Their simu lation shows improvement in the precision of reputation calculated for an entity.…”

Section: Trust Based Service Selectionmentioning

confidence: 99%

A Rough Sets-based Agent Trust Management Framework

Abedinzadeh

Sadaoui²

2013

IJISA

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Abstract-In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identify ing the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of mu lti agent systems. However, the features of the application do main must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user's request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain o f cross -language Web search. The resulting Web search system reco mmends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.

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Reputation measure approach of web service for service selection

Cited by 23 publications

References 24 publications

User-credibility Based Service Reputation Management for Service Selection

User-credibility Based Service Reputation Management for Service Selection

An adaptively emerging mechanism for context-aware service selections regulated by feedback distributions

A Rough Sets-based Agent Trust Management Framework

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