FluidRating: A time-evolving rating scheme in trust-based recommendation systems using fluid dynamics

Jiang, Wenjun; Wu, Jie; Wang, Guojun; Zheng, Huanyang

doi:10.1109/infocom.2014.6848108

Cited by 19 publications

(4 citation statements)

References 16 publications

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“…The key limitation of these models is that the trustworthiness of users within a community is indistinguishable [67], which limits the application of the models. Graph based models [29,47,62,69] assign a real number, ranging from 0 to 1, on each edge in the trust social network, and employ graph searching algorithms to evaluate the trustworthiness between any two users. The major limitation of these models is that trust is represented as a single value, which omits the uncertainty existing in trust and thus is inaccurate in assessing trust.…”

Section: Limitations Of Prior Artmentioning

confidence: 99%

Trust Assessment in Online Social Networks

Liu¹,

Yang

Wang

et al. 2021

IEEE Trans. Dependable and Secure Comput.

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Section: Limitations Of Prior Artmentioning

confidence: 99%

Trust Assessment in Online Social Networks

Liu¹,

Yang

Wang

et al. 2021

IEEE Trans. Dependable and Secure Comput.

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“…e idea of FluidRating is an important part of our model [12,13]. For a certain product, FluidRating divides the users into two categories.…”

Section: Fluidratingmentioning

confidence: 99%

“…is time the central route is more effective. On this basis, we express these two routes in the form of fluid mechanics from the idea of FluidRating [12,13]. Finally, the model was proposed, which we called ELMFluid.…”

Section: Introductionmentioning

confidence: 99%

User’s Attitude under the Perspective of Mental Energy Flow

Yang

Zhao

et al. 2020

Mathematical Problems in Engineering

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The analysis of the attitudes of online users plays an important role in real life and the future society, which can help us to analyze and predict the behavior of the users. The researchers also carried out a lot of related work. However, most of the previous classic methods searched the rules of the structure of the graph or the propagation path and did not consider the internal psychological dynamic mechanism of the user. In the context of big data, identifying online behavioral motivations of users through online information should be a future development trend. In this paper, we delve into the users’ internal psychological energy based on the Elaboration Likelihood Model and divide the user’s behavioral drive into the central route and the peripheral route. The degree of user’s processing of the known information and elaboration determines which route is more effective. The central route is mainly the user’s behavioral habits studied through user role theory. In the peripheral route, it calculates the influence of other users through a kind of hydromechanics algorithm—FluidRating. The final model we called ELMFluid which can express the flow of psychological energy through thermodynamics and hydromechanics. The experiments are performed using two real datasets in the end. The results show that the new model is superior to other algorithms.

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“…Moreover, based on trust evaluation schemes, many interesting models can be flexibly incorporated and many applications can be conducted. For instance, [30] proposes a rating prediction scheme in trust-based recommendation system, using fluid dynamics theory.…”

Section: B Trust Models In Osnsmentioning

confidence: 99%

Trust Models in Wireless Sensor Networks and Online Social Networks: A Comparative Study

Jiang

2014

2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems

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Wireless sensor networks (WSNs) are networks based on on the cooperation of small-sized nodes. Those nodes are mainly characterized by their low energy consumption, low cost, and wireless communication; Meanwhile, online social networks (OSNs) are becoming a popular way to meet people and keep in touch with friends. To improve the security performance in WSNs and the service quality in OSNs, trust models are commonly incorporated in both network environments. In this article, we present the first comparative study of trust models in WSNs and OSNs. We first provide a comparison of the features of WSNs and OSNs, and we provide a simple discussion of trust management in WSNs and OSNs. Next, we review and compare existing trust models for WSNs and OSNs in the literature. Finally, we conduct some discussion and point out the future directions, especially on how to enhance the trust management in a network by learning from the other type.

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FluidRating: A time-evolving rating scheme in trust-based recommendation systems using fluid dynamics

Cited by 19 publications

References 16 publications

Trust Assessment in Online Social Networks

Trust Assessment in Online Social Networks

User’s Attitude under the Perspective of Mental Energy Flow

Trust Models in Wireless Sensor Networks and Online Social Networks: A Comparative Study

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