Rater Credibility Research Articles

Evaluating single web service trust employing a three-level neuro-fuzzy system considering k-means clustering

Using Web services to assess data in a distributed configuration, apart from different hardware and software platforms for employing standard criteria, is practical because of development in the Internet and network infrastructure. Distributed applications can transfer data using web services. Trust is the main criterion to select the appropriate web service. Neuro-fuzzy systems including clustering are applied to assess the trust of single web services. This paper considers nine criteria including quality of service, subjective perspectives, user preference, credibility of raters, objective perspectives, dynamic computing, bootstrapping, independency and security. To obtain a neuro-fuzzy system with high prediction accuracy, the paper considers eight neuro-fuzzy membership functions (i.e., trapmf, gbellmf, trimf, gaussmf, dsigmf, psigmf, gauss2mf, pimf) using the k-means clustering. Also, to increase the speed and reduce the fuzzy rules, a three-level neuro-fuzzy system (13 neuro-fuzzy) is investigated. The main target of this paper is evaluating the trust of single web services using the nine aforementioned criteria, as web services selection is a main issue which is still absorbing researchers to conduct research works on this field and analyze it. Ultimately, the results show reasonable root mean square error (RMSE) amount, precision value, recall value, and F-score value. In comparison to previous research works, this study obtained the lower amounts of errors and presents the more accurate trust of single web services.

A Bayesian Multiagent Trust Model for Social Networks

In this paper, we introduce a framework for modeling the trustworthiness of peers in the setting of online social networks. In these contexts, it may be important to be filtering the wealth of messages that have been sent, which form the ongoing communication within a large community of users. This is achieved by constructing an intelligent agent that reasons about the message and each peer rater of the message, learning over time to properly gage whether a message is good or bad to show a user, based on message ratings, rater similarity, and rater credibility. Our approach employs a partially observable Markov decision process for trust modeling, moving beyond the more traditional adoption of probabilistic reasoning using beta reputation functions. In addition to outlining the technique in full, we present empirical results to demonstrate the effectiveness of our methods, both in simulations featuring head to head comparisons with competitors, and in the context of some existing online social networks where ground truth data are available.

Rater Credibility Assessment in Web Services Interactions

We investigate the problem of establishing trust in service-oriented environments. The focus is on providing an infrastructure for evaluating the credibility of raters in a reputation-based framewo...

A Reputation Mechanism for Business-to-Business Electronic Commerce That Accounts for Rater Credibility

This article investigates whether source credibility theory can support reputation mechanisms in business-to-business (B2B) electronic commerce. In contrast to consumer electronic marketplaces, the raters in B2B communities are skilled and connected, necessitating a reputation mechanism to account for the relationship between the user and the rater. To solve this problem, TrustBuilder, a prototype rating tool, incorporates a methodology to calculate a weighted rating based on source credibility theory. This solution offers several advantages over existing models. First, source credibility theory provides tested frameworks for aggregating ratings from different sources. Second, there are validated scales for measuring a source's (rater's) credibility. Finally, the weights of a rater's ratings depend on user preferences instead of rater behavior, which decreases the amount of data required to calibrate the model. In an experiment, industry practitioners evaluated bids from service providers using a credibility-weighted tool as well as a standard unweighted tool. The experiment showed that the use of a credibility-weighted tool led to increased user confidence as well as more varied evaluations. This study therefore provides evidence that incorporating source credibility theory in a rating tool adds value in the process of evaluating service providers by increasing the decision maker's confidence in the accuracy of the information.

Participant Input into Rater Selection: Potential Effects on the Quality and Acceptance of Ratings in the Context of 360-Degree Feedback

This article explores implications and potential benefits of giving individuals an opportunity to have input into the selection of raters involved in giving them 360-degree feedback. First, allowing participants some degree of input, but not total input into the selection of raters, may result in better perceptions of fairness, rater credibility and usefulness of the feedback. In addition, increased participation in the selection of raters could translate into the development of more challenging goals and higher levels of job performance. From a quantitative perspective, this could also produce more variance in the ratings.

Accounting for rater credibility when evaluating AEC subcontractors

In consumer electronic markets, rating mechanisms are important facilitators of trust between market participants. This paper investigates whether source credibility theory can support the evaluation of bidders in architecture/engineering/construction (AEC) electronic market places. In contrast to consumer electronic marketplaces, the raters in AEC communities are skilled and connected, necessitating a reputation mechanism to account for the relationship between the user and the rater. To solve this problem, TrustBuilder, a prototype rating tool, facilitates information sharing between peer industry practitioners by calculating a weighted rating based on source credibility theory. An experiment shows that AEC industry practitioners who evaluate bidding subcontractors trusted the information provided by TrustBuilder significantly more than information from a standard unweighted rating tool.