User-centric trust and reputation model for personal and trusted service selection

Abstract

Improving quality of services (QoS) through applying trust and reputation management technology is increasingly popular in the literature and industry. Most existing trust and reputation systems calculate a general trust value or vector based on the gathered feedback without regard to trust's locality and subjectivity; therefore, they cannot effectively support a personal selection with consumer preferences. Our goal is to build a trust and reputation mechanism for facilitating a trustworthy and personal service selection in a service-oriented Web, where service peers can act as a service provider and/or a service consumer. A user-centric trust and reputation mechanism distinguishing the different trust context and content to enable a personal service selection with regard to trust preference in a service-oriented Web is represented in detail. It is widely recognized that reputation-based trust methods must face the challenge of malicious behaviors. To deal with the malicious feedback behaviors, we introduce a "bidirectional" feedback mechanism based on QoS experience similarity in our trust and reputation framework. The test run demonstrates that our method can significantly increase the success rate of service transactions and is effective in resisting various malicious behaviors of service peers, when it is compared to other similar methods. C 2011 Wiley Periodicals, Inc.

A Customer-Centric Trust Evaluation Model for Personalized Service Selection

“…Most existing trust models calculate a general trust value or vector based on the gathered ratings without regard to the subjectivity of trust; therefore, they cannot effectively support personalized service selection with customer preferences and expectations [15]. We highlight the motivation of this paper through a motivating example below.…”

“…In [23,24], Malik and Bouguettaya allowed the service consumers to calculate the reputation scores of the Web services according to their own personal preferences. Yan et al [15] built a user-centric trust and reputation mechanism that distinguishes the different trust context and content to enable a personal service selection with regard to trust preference. Deng et al [25] proposed a novel method to provide personalized service recommendations to individual customers based on trust relationships between customers and services.…”

“…Trust evaluation should also take into account the individual differences of customers. A common characteristic of existing trust evaluation models is that they provide global trust values, meaning that all the customers in an online community will see the same trust value for a specific service [15]. However, each consumer is an independent individual that has his/her own trust preferences and expectations.…”

“…A more feasible trust evaluation model for personalized service selection with regard to consumer preferences and expectations should be created. The final trust value should reflect a personalized trust status from an individual consumer's perspective [15]. This paper proposes a customer-centric trust evaluation model for personalized services selection based on peerto-peer trust management.…”

An Automatic Trust Calculation Based on the Improved Kalman Filter Detection Algorithm

“…Several trust-based systems [5,14,15,21] have been proposed, however the authors in [5] did not focus on detecting malicious consumers. Although they provided high flexibility for consumers to use a variety of scoring functions over the same data for personalized reputation evaluation, this approach is based on the assumption that consumers do not mask their malicious behaviour, meaning that it is hard to detect those malicious service consumers that behave well until they gain good trust values and then behave maliciously.…”