Applying and Inferring Fuzzy Trust in Semantic Web Social Networks

Lesani, Mohsen; Bagheri, Saeed

doi:10.1007/978-0-387-34347-1_3

Cited by 19 publications

(11 citation statements)

References 3 publications

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“…Fuzzy logic [29,65] is very well-suited to represent such natural language labels which represent vague intervals rather than exact values. For instance, in [59] and [31], fuzzy linguistic terms are used to specify the trust in agents in a P2P network, and in a social network, respectively. A classical example of trust as a gradual notion can be found in [1], in which a four-value scale is used to determine the trustworthiness of agents, viz.…”

Section: Trust Representationmentioning

confidence: 99%

“…Most approaches completely ignore distrust (see for example [31,32,43,55,66]), or consider trust and distrust as opposite ends of the same continuous scale (see e.g. [1,19,59]).…”

Section: Trust Representationmentioning

confidence: 99%

“…Other trust propagation work includes techniques based on fuzzy if-then rules [31,59], on the theory of spreading activation models (Ziegler and Lausen's Appleseed [68]), or on the semantic distance between a TTP's trust and a user's perception of the TTP's trust [1].…”

Section: Propagationmentioning

confidence: 99%

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Trust and Recommendations

Victor

Cock

Cornelis

2010

Recommender Systems Handbook

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Recommendation technologies and trust metrics constitute the two pillars of trust-enhanced recommender systems. We discuss and illustrate the basic trust concepts such as trust and distrust modeling, propagation and aggregation. These concepts are needed to fully grasp the rationale behind the trust-enhanced recommender techniques that are discussed in the central part of the chapter, which focuses on the application of trust metrics and their operators in recommender systems. We explain the benefits of using trust in recommender algorithms and give an overview of state-of-the-art approaches for trust-enhanced recommender systems. Furthermore, we explain the details of three well-known trust-based systems and provide a comparative analysis of their performance. We conclude with a discussion of some recent developments and open challenges, such as visualizing trust relationships in a recommender system, alleviating the cold start problem in a trust network of a recommender system, studying the effect of involving distrust in the recommendation process, and investigating the potential of other types of social relationships.

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Section: Trust Representationmentioning

confidence: 99%

“…Most approaches completely ignore distrust (see for example [31,32,43,55,66]), or consider trust and distrust as opposite ends of the same continuous scale (see e.g. [1,19,59]).…”

Section: Trust Representationmentioning

confidence: 99%

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Trust and Recommendations

Victor

Cock

Cornelis

2010

Recommender Systems Handbook

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“…The trust and trust network have four obvious properties: 1) Asymmetry; 2) Transitivity; 3) Composability: people naturally compose the trust value when they receive them from different sources by giving higher importance to more trusted sources [17]; 4) Decay: the trust decays as the number of transitivity hops increases along a social trust path [18], [19].…”

Section: B Trust Network and Fuzzy Trust Networkmentioning

confidence: 99%

“…They have shown that there is a correlation between the profile similarity of users and their trust. Lesani and Bagheri [17] proposed a fuzzy trust inference mechanism using fuzzy linguistic terms to specify the trust to other users and presented an algorithm for inferring the trust from one user to another that may not be directly connected in the trust graph of a social network. It is a desirable way for users to obtain and understand the trust values with the fuzzy linguistic expressions.…”

Section: Introductionmentioning

confidence: 99%

MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks

Hao

Min

Lin

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Mobile Social Networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the non-semantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. Firstly, a mobile context including an intersection of prestige of users, location, time and social context is constructed. Secondly, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is utilized to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.

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Multi‐dimensional fuzzy trust evaluation for mobile social networks based on dynamic community structures

Chen

Wang

Yan

et al. 2016

Concurrency and Computation

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Summary As mobile social networks (MSNs) are booming and gaining tremendous popularity, there have been an increasing number of communications and interactions among users. Taking this advantage, users in MSNs make decisions via collecting and combining trust information from different users. Hence, trust evaluation technology has become a key requirement for network security in MSNs. In such MSNs, however, the community/group structures are dynamically changing, and users may belong to multiple communities/groups. Therefore, trust evaluation plays a critical role in inferring trustworthy contacts among users. In this paper, an innovative trust inference model is proposed for MSNs, in which multiple dimensional trust metrics are incorporated to reflect the complexity of trust. To infer trust relations between users in MSNs with complex communities, we first construct dynamic implicit social behavioral graphs (DynISBG) based on dynamic complex community/group structures and propose an efficient detection algorithm for DynISBG under fuzzy degree κ. We then present a multi‐dimensional fuzzy trust inferring approach that involves four metrics, that is, static attribute trust factor, dynamic behavioral trust factor, long‐term trust evolution factor, and recommendation‐based trust opinion. Moreover, to obtain the recommendation‐based trust opinion about indirect connected users, we discuss the trust aggregation and propagation along trust path. Finally, we evaluate the performance of our novel approach with simulations. The results show that, compared with the existing approaches, the proposed model provides a more detailed analysis in trust evaluation with higher accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

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Applying and Inferring Fuzzy Trust in Semantic Web Social Networks

Cited by 19 publications

References 3 publications

Trust and Recommendations

Trust and Recommendations

MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks

Multi‐dimensional fuzzy trust evaluation for mobile social networks based on dynamic community structures

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