A computational model for trust and reputation relationship in social network

Alam, Faruk; Paul, Arnab

doi:10.1109/icrtit.2016.7569559

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…Faruk and Arnab proposed a trust management model which will take factors like direct, indirect and global trust of the service to find out the final trust value of the service. They take the trust path distance into consideration while calculating the indirect trust [24]. Li et al [25] propose two trust models to content delivery network: a local trust model and a cross-domain trust model.…”

Section: Related Work a Trust Computationmentioning

confidence: 99%

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Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

Zhang

Zhong

2019

IEEE Access

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Consumers' reviews in E-commerce systems are usually treated as the important resources that reflect user's experience, feelings, and willingness to purchase items. All this information may involve consumers' views on things that can express interest, sentiments, and opinions. Many kinds of research have shown that people are more likely to trust each other with the same attitude toward similar things. In this paper, we consider seeking and accepting sentiments and suggestions in E-commerce systems somewhat implies a form of trust between consumers during shopping. Following this view of point, an E-commerce system reviews mining oriented sentiment similarity analysis approach is put forward to exploring users' similarity and their trust. We divide the trust into two categories, namely direct trust, and propagation of trust, which represents a trust relationship between two individuals. The direct trust degree is obtained from sentiment similarity, and we present an entity-sentiment word pair mining method for similarity feature extraction. The propagation of trust is calculated according to the transitivity feature. Using the proposed trust representation model, we use the shortest path to describe the tightness of trust and put forward an improved shortest path algorithm to figure out the propagation trust relationship between users. A large-scale E-commerce website reviews dataset is collected to examine the accuracy of the algorithms and feasibility of the models. The experimental results indicate that the sentiment similarity analysis can be an efficient method to find trust between users in E-commerce systems. INDEX TERMS E-commerce reviews, entity-sentiment word pair, sentiment similarity, trust.

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Section: Related Work a Trust Computationmentioning

confidence: 99%

“…(23). G, is trust network graph established by direct trust links and G is a subgraph of G. (24). PR u a ,u b , is propagation trust of two users where there is no direct trust.…”

Section: Sentiment Similarity and Trust Related Definitions A Smentioning

confidence: 99%

Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

Zhang

Zhong

2019

IEEE Access

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“…However, such definition cannot lay claim to completeness, since notions of trust and reputation as applied to peculiarities of Internetbased activities must be defined in a more context-specific way. Among other things, Alam & Paul define trust as «a belief, the trusting agent has in the trusted agent's willingness and capability to deliver the services that they are mutually agreed on in a given context and in a given time slot» [13]. In addition, Wang & Vassileva associate term 'trust' with «a peer's belief in another peer's capabilities, honesty and reliability based on its own direct experiences» [14].…”

Section: Trust and Reputation What Is The Difference Between These Tmentioning

confidence: 99%

“…According to [14], reputation is defined as «peer's belief in another peer's capabilities, honesty and reliability based on recommendations received from other peers». On the other hand, already cited above Alam & Paul propose to consider reputation as «aggregation of all recommendations provided by the third-party recommendation agents about the quality of the trusted agent» [13]. Abdul-Rahman & Hailes define reputation as «an expectation about an agent's behavior based on information about its past behavior» [16].…”

Section: Trust and Reputation What Is The Difference Between These Tmentioning

confidence: 99%

Reputation Systems in E-commerce: Comparative Analysis and Perspectives to Model Uncertainty Inherent in Them

Nosovskiy¹,

Degtiarev

2019

Proceedings of ISP RAS

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E-commerce is a runaway activity growing at an unprecedented rate all over the world and drawing millions of people from different spots on the globe. At the same time, e-commerce affords ground for malicious behavior that becomes a subject of principal concern. One way to minimize this threat is to use reputation systems for trust management across users of the network. Most of existing reputation systems are feedbackbased, and they work with feedback expressed in the form of numbers (i.e. from 0 to 5 as per integer scale). In general, notions of trust and reputation exemplify uncertain (imprecise) pieces of information (data) that are typical for the field of e-commerce. We suggest using fuzzy logic approach to take into account the inherent vagueness of user's feedback expressing the degree of satisfaction after completion of a regular transaction. Brief comparative analysis of well-known reputation systems, such as EigenTrust, HonestPeer, Absolute Trust, PowerTrust and PeerTrust systems is presented. Based on marked out criteria like convergence speed, robustness, the presence of hyper parameters, the most robust and scalable algorithm is chosen on the basis of carried out sets of computer experiments. The examples of chosen algorithm's (PeerTrust) fuzzy versions (both Type-1 and Interval Type-2 cases) are implemented and analysed.

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Reputation model based on Bayesian theory and eigenvector in complex networks

Zahi

Hasson

2023

The SECOND INTERNATIONAL SCIENTIFIC CONFERENCE (SISC2021): College of Science, Al-Nahrain University

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A computational model for trust and reputation relationship in social network

Cited by 8 publications

References 2 publications

Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

Reputation Systems in E-commerce: Comparative Analysis and Perspectives to Model Uncertainty Inherent in Them

Reputation model based on Bayesian theory and eigenvector in complex networks

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