Proposed Reputation Model Research Articles

Novel hybrid model for organizations’ reputation in online social networks

This research presents a hybrid model for measuring reputation of organizations in online social networks (OSNs). The model combines quantitative and qualitative features that analyze OSN link structures, interactions, and users’ sentiments to identify and evaluate how well an organization is perceived in a social network. The proposed reputation model is novel as it introduces several new features and a comprehensive set of metrics to calculate reputation and rank organizations. The model uses multi criteria decision making techniques to integrate the different features into one comprehensive reputation metric. The model has been implemented and applied to calculate the reputation of 47 famous worldwide companies using a dataset of more than 1.4 million posts and 7.6 million profiles collected from Twitter. The experiment shows that the proposed model outperforms other existing models and techniques as it scores the least error and the highest correlation (high accuracy) compared to a benchmark of the top 10 world’s most admired companies.

Detecting Nuisance Calls over Internet Telephony Using Caller Reputation

Internet telephony permit callers to manage self-asserted profiles without any subscription contract nor identification proof. These cost-free services have attracted many telemarketers and spammers who generate unsolicited nuisance calls. Upon detection, they simply rejoin the network with a new identity to continue their malicious activities. Nuisance calls are highly disruptive when compared to email and social spam. They not only include annoying telemarketing calls but also contain scam and voice phishing which involves security risk for subscribers. Therefore, it remains a major challenge for Internet telephony providers to detect and avoid nuisance calls efficiently. In this paper, we present a new approach that uses caller reputation to detect different kinds of nuisance calls generated in the network. The reputation is computed in a hybrid manner by extracting information from call data records and using recommendations from reliable communicating participants. The behavior of the caller is assessed by extracting call features such as call-rate, call duration, and call density. Long term and short term reputations are computed to quickly detect the changing behavior of callers. Furthermore, our approach involves an efficient mechanism to combat whitewashing attacks performed by malicious callers to continue generating nuisance calls in the network. We conduct simulations to compute the performance of our proposed model. The experiments conclude that the proposed reputation model is an effective method to detect different types of nuisance calls while avoiding false detection of legitimate calls.

A blockchain-based group formation strategy for optimizing the social reputation capital of an IoT scenario

The “Internet of Things” (IoT) provide humans and smart objects with attractive services, based on the advanced features of the IoT devices, like high sensing, real-time acting and reasoning.In our previous research we have highlighted that these features can be improved by promoting cooperation between smart objects, and we introduced the association between Multi-Agent Systems and IoT devices. In that context, we focused on the issue of accurately choosing the best partners for cooperation, in a scenario composed by several federations. We proposed a reputation model and we have shown that the model leads to detect agents having unreliable or misleading behaviors and that the model itself can be profitably used to form groups of agents that mutually cooperate for improving the effectiveness of their tasks. In this further contribution, we focus on the important issue of the group formation, by arguing that in practical IoT situations it is necessary to improve the group formation strategy to provide it with greater adaptability. To this end we introduce – in a particular IoT context described in this work – a two-phase group formation algorithm to support the reputation model. Experimental results prove that the adoption of the group formation algorithm, along with the proposed reputation model provides a few benefits to the whole IoT ecosystem.

Online Reputation Model Using Multiple Quality Factors

Users on the internet are looking for ways to minimize their experiences in performing online transactions. Reputation systems as a decision support tool are trying to facilitate online transactions. However, many reputation systems use Naí¯ve methods to compute the reputation of an item. These methods are unstable when there is sparsity in the ratings. Also, they cannot discover trends emerging from recent ratings. Other methods, which use weighted average or probabilistic model, usually focus on one aspect of the reviewer ratings. Even though models that combine multiple factors often accomplish that through an arbitrary set of weights. This research study looks at various aspects of reviewers’ ratings and proposes a new reputation model that attempts to assess the reviewer reputation by combining four factors through a Fuzzy model. These weights are then involved in computing the item reputation. The proposed reputation model has been validated against state-of-art reputation models and presented significant accuracy regarding Mean Absolute Errors (MAE) and Kendall correlation. The proposed reputation model also works well with the sparse and dense dataset.

A reputation based electronic government procurement model

Each year, governments spend billions in procurement. In this context, they generally select their suppliers based on the minimum price. However, other criteria than price may be considered to help governments minimize the procurement costs. This paper proposes a formal reputation model that is intended to determine the winners of a public procurement process. The proposed model combines three elements: (1) the direct reputation of the supplier, (2) the indirect reputation of the supplier, and (3) the difference in beliefs between the government and the supplier. The proposed model compares situations where reputation is integrated along price for selecting suppliers and where it is not. Results show that the proposed reputation model may lead to lower costs for governments.

Evaluating Reputation of Agents in E-commerce Multi-Agent Environments

This paper aims to enhance trust in e-commerce multiagent systems by presenting a model to evaluate reputation of provider agents. In this case, we study the most representative trust models in multi-agent systems, which provide different methods for calculating reputation. According to these analysis criteria, a new approach is presented to compute the reputation of provider agents. To evaluate the proposed reputation model, an experimental was carried out in two stages. First, the average accuracy of model in computing the reputation was evaluated by simulating the proposed approach in a multi-agent environment. Second, the performance of the model was compared with a multi-agent environment which does not apply the proposed model. The experimental results show that the proposed reputation model can evaluate the reputation of providers accurately, and the comparison demonstrates that the proposed model can significantly choose the trustworthy provider agent. The ultimate goal of this study is to present a model for computing the reputation of provider agents, and selecting the trustworthy provider based on this computation. We believe that the proposed model will be beneficial to enhance trust in e-commerce environments.

A graph-based comprehensive reputation model: Exploiting the social context of opinions to enhance trust in social commerce

Social commerce is a promising new paradigm of e-commerce. Given the open and dynamic nature of social media infrastructure, the governance structures of social commerce are usually realized through reputation mechanisms. However, the existing approaches to the prediction of trust in future interactions are based on personal observations and/or publicly shared information in social commerce application. As a result, the indications are unreliable and biased because of limited first-hand information and stakeholder manipulation for personal strategic interests. Methods that extract trust values from social links among users can improve the performance of reputation mechanisms. Nonetheless, these links may not always be available and are typically sparse in social commerce, especially for new users. Thus, this study proposes a new graph-based comprehensive reputation model to build trust by fully exploiting the social context of opinions based on the activities and relationship networks of opinion contributors. The proposed model incorporates the behavioral activities and social relationship reputations of users to combat the scarcity of first-hand information and identifies a set of critical trust factors to mitigate the subjectivity of opinions and the dynamics of behaviors. Furthermore, we enhance the model by developing a novel deception filtering approach to discard “bad-mouthing” opinions and by exploiting a personalized direct distrust (risk) metric to identify malicious providers. Experimental results show that the proposed reputation model can outperform other trust and reputation models in most cases.

Computational reputation model based on selecting consensus choices: An empirical study on semantic wiki platform

In online social media, social interactions among users are an important process to consensus building. Particularly, wiki platforms are one of the most powerful tools for encouraging and managing such social collaborations. These wiki platforms can be extended to collaborative knowledge management system. However, since the wiki systems allow users to use their own semantics and backgrounds for describing resources, there are often conflicts among users who describe the same wiki resources in different ways. Thereby, the aim of this paper is (i) to formalize such conflicts, and (ii) to rationally select consensus choice by measuring reputation scores of the wiki users. Especially, in this study, we have designed a semantic wiki platform to more efficiently represent the conflicts, and we have evaluated the proposed reputation model by analyzing how the conflicts have been reconciled (e.g., length of a social interaction cycle).

RRM: An incentive reputation model for promoting good behaviors in distributed systems

Reputation systems represent soft security mechanisms that complement traditional information security mechanisms. They are now widely used in online e-commerce markets and communities in order to stimulate good behaviors as well as to restrain adverse behaviors. This paper analyzes the limitations of the conversational reputation models and proposes an incentive reputation model called the resilient reputation model (RRM) for the distributed reputation systems. The objective of this reputation model is not only to encourage the users to provide good services and, therefore, to maximize the probability of good transaction outcomes, but also to punish those adverse users who are trying to manipulate the application systems. The simulation results indicate that the proposed reputation model (RRM) could effectively resist against the common adverse behaviors, while protecting the profits of sincere users from being blemished by those adversaries.

Fuzzy computational models for trust and reputation systems

In the recent past, a considerable research has been devoted to trust and reputation mechanisms to simplify complex transactions for open environments in social networking, e-commerce, and recommender systems (RS). In real life, we come to know about others through our social circle according to their reputation which is a public view. However, it is not always adequate to depend solely on the public view and therefore a trust measure is required to give a personalized view of the future encounters with a specific partner. In this paper, we propose fuzzy computational models for both trust and reputation concepts. Reciprocity and experience are used for trust modeling while the proposed reputation model is a fuzzy extension of beta reputation model. A two-level filtering methodology is proposed to benefit to a large extent from both the concepts separately. In order to justify the proposed models, we compared them with the existing reputation models for movie RS. The experimental results show that the incorporation of trust and reputation concepts into RS indeed improves the recommendation accuracy and establish that our models are better than beta and the popular eBay reputation models.

A user reputation model for a user‐interactive question answering system

AbstractIn this paper, we propose a user reputation model and apply it to a user‐interactive question answering system. It combines the social network analysis approach and the user rating approach. Social network analysis is applied to analyze the impact of participant users' relations to their reputations. User rating is used to acquire direct judgment of a user's reputation based on other users' experiences with this user. Preliminary experiments show that the computed reputations based on our proposed reputation model can reflect the actual reputations of the simulated roles and therefore can fit in well with our user‐interactive question answering system. Copyright © 2006 John Wiley & Sons, Ltd.