Comparative Study of Trust Modeling for Automatic Landmark Tagging

Ivanov, Ivan; Vajda, Péter; Korshunov, Pavel; Ebrahimi, Touradj

doi:10.1109/tifs.2013.2242889

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…An example includes the work by Adali et al [18] who http://www.journaloftrustmanagement.com/content/1/1/6 assessed trust between two users based on the amount of conversation and the propagation of messages within the Twitter social network. Other approaches derive trust that is given to users from community-based reputation or social feedback, see, for example, the work by Ivanov et al [19].…”

Section: Computational Models Of Trustmentioning

confidence: 99%

Trust-based Decision-making for the Adaptation of Public Displays in Changing Social Contexts

Wißner

Hammer

Kurdyukova

et al. 2014

Journal of Trust Management

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Public displays may adapt intelligently to the social context, tailoring information on the screen, for example, to the profiles of spectators, their gender or based on their mutual proximity. However, such adaptation decisions should on the one hand match user preferences and on the other maintain the user's trust in the system. A wrong decision can negatively influence the user's acceptance of a system, cause frustration and, as a result, make users abandon the system. In this paper, we propose a trust-based mechanism for automatic decision-making, which is based on Bayesian Networks. We present the process of network construction, initialization with empirical data, and validation. The validation demonstrates that the mechanism generates accurate decisions on adaptation which match user preferences and support user trust.

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Section: Computational Models Of Trustmentioning

confidence: 99%

Trust-based Decision-making for the Adaptation of Public Displays in Changing Social Contexts

Wißner

Hammer

Kurdyukova

et al. 2014

Journal of Trust Management

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“…Ivanov et al, [6] design a method of automatic geotag propagation in online social network images based on its content and context. Well known landmarks and geotags associated with them can be incorrect or a spit tag that damages the integrity and reliability of the propagation system.…”

Section: Trustworthy Large Scale Social Network Evaluationmentioning

confidence: 99%

Trust Aware System for Social Networks: A Comprehensive Survey

Manasa¹,

Manjula²,

Venugopal³

2017

IJCA

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Social networks are the platform for the users to get connected with other social network users based on their interest and life styles. Existing social networks have millions of users and the data generated by them are huge and it is difficult to differentiate the real users and the fake users. Hence a trust worthy system is recommended for differentiating the real and fake users. Social networking enables users to send friend requests, upload photos and tag their friends and even suggest them the web links based on the interest of the users. The friends recommended, the photos tagged and web links suggested may be a malware or an untrusted activity. Users on social networks are authorised by providing the personal data. This personal raw data is available to all other users online and there is no protection or methods to secure this data from unknown users. Hence to provide a trustworthy system and to enable real users activities a review on different methods to achieve trustworthy social networking systems are examined in this paper.

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“…Other approaches derive trust that is given to users from community-based reputation or social feedback (e.g. Ivanov et al (2013)). …”

Section: Computational Models Of Trustmentioning

confidence: 99%

Trust-based decision-making for smart and adaptive environments

Hammer

Wiβner

André

2015

User Model User-Adap Inter

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Smart environments are able to support users during their daily life. For example, smart energy systems can be used to support energy saving by controlling devices, such as lights or displays, depending on context information, such as the brightness in a room or the presence of users. However, proactive decisions should also match the users' preferences to maintain the users' trust in the system. Wrong decisions could negatively influence the users' acceptance of a system and at worst could make them abandon the system. In this paper, a trust-based model, called User Trust Model (UTM), for automatic decision-making is proposed, which is based on Bayesian networks. The UTM's construction, the initialization with empirical data gathered in an online survey, and its integration in an office setting are described. Furthermore, the results of a live study and a live survey analyzing the users' experience and acceptance are presented.

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Comparative Study of Trust Modeling for Automatic Landmark Tagging

Cited by 10 publications

References 24 publications

Trust-based Decision-making for the Adaptation of Public Displays in Changing Social Contexts

Trust-based Decision-making for the Adaptation of Public Displays in Changing Social Contexts

Trust Aware System for Social Networks: A Comprehensive Survey

Trust-based decision-making for smart and adaptive environments

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