Group affinity based social trust model for an intelligent movie recommender system

2011 ◽  
Vol 64 (2) ◽  
pp. 505-516 ◽  
Author(s):  
Mucheol Kim ◽  
Sang Oh Park
Keyword(s):  
Recommender System ◽  
Social Trust ◽  
Trust Model

A Trust Model for Detecting Device Attacks in Mobile Ad Hoc Ambient Home Network

2016 ◽  
Vol 8 (2) ◽  
pp. 16-37
Author(s):  
Akinboro Solomon ◽  
Emmanuel Olajubu ◽  
Ibrahim Ogundoyin ◽  
Ganiyu Aderounmu
Keyword(s):  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Metrics ◽  
Social Trust ◽  
Trust Model ◽  
Convergence Time ◽  
Home Network ◽  
Detection Accuracy ◽  
Malicious Nodes ◽  
Mobile Ad Hoc

This study designed, simulated and evaluated the performance of a conceptual framework for ambient ad hoc home network. This was with a view to detecting malicious nodes and securing the home devices against attacks. The proposed framework, called mobile ambient social trust consists of mobile devices and mobile ad hoc network as communication channel. The trust model for the device attacks is Adaptive Neuro Fuzzy (ANF) that considered global reputation of the direct and indirect communication of home devices and remote devices. The model was simulated using Matlab 7.0. In the simulation, NSL-KDD dataset was used as input packets, the artificial neural network for packet classification and ANF system for the global trust computation. The proposed model was benchmarked with an existing Eigen Trust (ET) model using detection accuracy and convergence time as performance metrics. The simulation results using the above parameters revealed a better performance of the ANF over ET model. The framework will secure the home network against unforeseen network disruption and node misbehavior.

AutoTrustRec: Recommender System with Social Trust and Deep Learning using AutoEncoder

2020 ◽  
Vol 79 (29-30) ◽  
pp. 20845-20860 ◽  
Author(s):  
Gourav Bathla ◽  
Himanshu Aggarwal ◽  
Rinkle Rani
Keyword(s):  
Deep Learning ◽  
Recommender System ◽  
Social Trust

Group-Aware Social Trust Management for a Movie Recommender System

2011 ◽  
pp. 495-501 ◽  
Author(s):  
Mucheol Kim ◽  
Young-Sik Jeong ◽  
Jong Hyuk Park ◽  
Sang Oh Park
Keyword(s):  
Recommender System ◽  
Trust Management ◽  
Social Trust

A Social Trust Model Considering Trustees’ Influence

2014 ◽  
pp. 357-364 ◽  
Author(s):  
Jian-Ping Mei ◽  
Han Yu ◽  
Yong Liu ◽  
Zhiqi Shen ◽  
Chunyan Miao
Keyword(s):  
Social Trust ◽  
Trust Model

scholarly journals A genre trust model for defending shilling attacks in recommender systems

2021 ◽  
Author(s):  
Li Yang ◽  
Xinxin Niu
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Trust Model ◽  
Trust Value ◽  
Shilling Attack ◽  
Trust Degree ◽  
Different Types ◽  
And Behavior ◽  
The Impact

AbstractShilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of items and take both trust value and user credibility into consideration. This paper introduces different types of shilling attack methods in an attempt to study the impact of users’ trust values and behavior features on defending against shilling attacks. Meanwhile, it improves the approach used to calculate user similarities to form a recommendation model based on genre trust degrees. The performance of the genre trust-based recommender system is evaluated on the Ciao dataset. Experimental results demonstrated the superior and comparable genre trust degrees recommended for defending against different types of shilling attacks.

A Model of Online Trust Among Adolescents

2019 ◽  
Vol 9 (2) ◽  
pp. 34-50
Author(s):  
Avin Fadilla Helmi ◽  
Wahyu Widhiarso ◽  
Adelia Khrisna Putri ◽  
Ramadhan Dwi Marvianto ◽  
Acintya Ratna Priwati ◽  
...  
Keyword(s):  
Social Support ◽  
High School Students ◽  
Significant Influence ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Social Trust ◽  
Self Esteem ◽  
Trust Model ◽  
Self Disclosure ◽  
Online Trust

This study identified factors that contribute to adolescents' online trust. Two hypotheses are stated: (1) there is a significant influence of social loneliness and self-esteem toward self-disclosure; and (2) social support has a significant influence toward online trust, with self-disclosure as a mediator. 205 high school students completed a survey covering four self-rated scales: online trust, self-disclosure, social support, and loneliness. Each scale met the psychometric standards of validity and reliability. Data was analyzed using structural equation modelling (SEM). The findings confirmed both hypotheses. The Goodness of Fit as indicated by a chi-square coefficient of 345.06 (p = .00); RMSEA: .04; CFI: .98; and TLI: .98, exceeded the set criteria. The main determinant of the direct effect of social trust and online trust model (β = .35**) was self-disclosure, a mediator on the effect of social support toward online trust (β = .05). The variables which directly influenced self-disclosure were loneliness (β = -.31**), self-esteem (β = .14), and social support (β = .24**).

scholarly journals Identity management-based social trust model for mediating information sharing and privacy enhancement

2011 ◽  
Vol 5 (8) ◽  
pp. 887-897 ◽  
Author(s):  
Mucheol Kim ◽  
Jiwan Seo ◽  
Sanghyun Noh ◽  
Sangyong Han
Keyword(s):  
Information Sharing ◽  
Identity Management ◽  
Social Trust ◽  
Trust Model

scholarly journals Towards Improving Recommender System: A Social Trust-Aware Approach

2015 ◽  
Vol 7 (2) ◽  
pp. 8-15 ◽  
Author(s):  
Naziha Abderrahim ◽  
◽  
Sidi Mohamed Benslimane
Keyword(s):  
Recommender System ◽  
Social Trust ◽  
System A
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