A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling

Martínez-Cruz, Carmen; Porcel, Carlos; Bernabé-Moreno, Juan; Herrera‐Viedma, Enrique

doi:10.1016/j.ins.2015.03.013

Cited by 113 publications

(51 citation statements)

References 39 publications

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“…In this section, we elaborate on the proposed Category-based Context-aware Reputation Mechanism (CCRM), which integrates the reputation evaluation with data category [17,23], context-awareness technologies [19] and the VCG mechanism [7,18,24] to defend against the insider threat and enhance the data veracity in MCC. The CCRM is implemented in both mobile clients and cloud service providers to perform bidirectional reputation evaluation.…”

Section: Category-based Context-aware Reputation Mechanism (Ccrm)mentioning

confidence: 99%

Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

Lin

Tian

et al. 2017

Information Sciences

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Abstract:As a promising next-generation computing paradigm, Mobile Cloud Computing (MCC) enables the large-scale collection and big data processing of personal private data. An important but often overlooked V of big data is data veracity, which ensures that the data used are trusted, authentic, accurate and protected from unauthorized access and modification. In order to realize the veracity of data in MCC, specific trust models and approaches must be developed. In this paper, a Category-based Context-aware and Recommendation incentive-based reputation Mechanism (CCRM) is proposed to defend against internal attacks and enhance data veracity in MCC. In the CCRM, innovative methods, including a data category and context sensing technology, a security relevance evaluation model, and a Vickrey-Clark-Groves (VCG)-based recommendation incentive scheme, are integrated into the process of reputation evaluation. Cost analysis indicates that the CCRM has a linear communication and computation complexity. Simulation results demonstrate the superior performance of the CCRM compared to existing reputation mechanisms under internal collusion attacks and bad mouthing attacks.

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Section: Category-based Context-aware Reputation Mechanism (Ccrm)mentioning

confidence: 99%

Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

Lin

Tian

et al. 2017

Information Sciences

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“… Hybrid recommender systems: These systems use a combination of the aforementioned methods to offer recommendations. 16,[35][36][37] Other types of recommender systems include community-based, demographics-based, and utilitybased recommender systems 38 that are all classified according to input and background data and the algorithms employed to generate recommendations. 16,37 …”

Section: Types Of Recommender Systems Based On Their Techniquesmentioning

confidence: 99%

Recommender system introduction for requests of cancer world

Aalipour

Ghazisaeedi

2017

Int J Community Med Public Health

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Awareness of cancer patients and their families, health care providers, and specialized cancer centers is achieved through access to up-to-date information about various items. Today, intelligent information technology systems have an important role in the awareness of people. Therefore, a type of technology is required that is capable of learning people’s needs, interests and suggesting appropriate information accordingly. The emergence of information technology systems, like recommender systems, is a step towards selecting appropriate information. With modeling the preferences, interests, needs, requests, and behaviors of the users, recommender systems seek to predict the future preferences, needs, and behaviors of the users to recommend appropriate and helpful services accordingly. Recommender systems can be a suitable tool for the information management of cancer-related screenings, diagnoses, treatments, operations, and rehabilitation programs. Access to treatment and health recommendations from valid sources is an important component of the natural processes of human decision making. The aim of this collection is to introduce recommender systems to use in cancer-related issues.

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“…It should be mentioned that the demand emerged for creation of methods for study and mastering of new linguistic tools and models with the purpose of their correct practical application (Martinez-Cruz, C. et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%