Towards trust-based recommender systems for online software services

Gallege, Lahiru S.; Gamage, Dimuthu U.; Hill, James H.; Raje, Rajeev R.

doi:10.1145/2602087.2602118

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…This service is used mainly as a recommendation driver and has a significant impact; by using it, numerous new connections have been created. Further work by [60] and [61] propose trusted-based recommendation techniques.…”

Section: Generic-based Trustworthiness Approachesmentioning

confidence: 99%

Credibility Analysis in Social Big Data

Abu-Salih

Wongthongtham

Zhu

et al. 2021

Social Big Data Analytics

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The concept of social trust has attracted an attention of information processors/data scientists and information consumers / business firms. One of the main reasons for acquiring the value of SBD is to provide frameworks and methodologies using which the credibility of online social services users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of social big data. Given the exposed environment's settings and fewer limitations related to online social services, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. This chapter presents an overview of the notion of credibility in the context of SBD. It also list an array of approaches to measure and evaluate the trustworthiness of users and their contents. Finally, a case study is presented that incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

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Section: Generic-based Trustworthiness Approachesmentioning

confidence: 99%

Credibility Analysis in Social Big Data

Abu-Salih

Wongthongtham

Zhu

et al. 2021

Social Big Data Analytics

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“…Lack of a sentiment analysis of conversations: In the context of social trust, frameworks have been developed to analyse the users' content, taking into consideration the overall feelings regarding what they have chosen to expose their content [17,19,51]. However, these efforts did not attempt a sentiment analysis of a post's replies by measuring the trustworthiness values.…”

Section: Lack Of An Advanced Domain-based Trustworthiness Approachesmentioning

confidence: 99%

“…This service is used mainly as a recommendation driver and has a significant impact; by using it, numerous new connections have been created. Further work by Gallege et al [14] and Sun et al [15] propose trusted-based recommendation techniques.…”

Section: Generic-based Trustworthiness Approachesmentioning

confidence: 99%

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Abu-Salih

Bremie

Wongthongtham

et al. 2019

Advances in Intelligent Systems and Computing

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The wealth of Social Big Data(SBD) represents a unique opportunity for organisations to obtain the excessive use of such data abundance to increase their revenues. Hence, there is an imperative need to capture, load, store, process, analyse, transform, interpret, and visualise such manifold social datasets to develop meaningful insights that are specific to an application's domain. This paper lays the theoretical background by introducing the state-ofthe-art literature review of the research topic. This is associated with a critical evaluation of the current approaches, and fortified with certain recommendations indicated to bridge the research gap.

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“…‐ Principle 4 (trust‐based specification): A specification of a Webcam service will indicate, in a formal manner such as a trust‐contract , the functional and non‐functional attributes of that service along with, for example, the [B, D, U]‐based quantification of these non‐functional attributes. The [B, D, U] tuple associated with the ability of the Webcam service to meet the response time deadlines may be quantified as (0.8, 0.1, 0.1) based on the evidences available from the empirical testing of that Webcam service by the developer ( i.e., the internal view) and the reviews provided by the users of this Webcam service ( i.e., the external view).…”

Section: Application Of Trust Principles To the Case Study (Section 2)mentioning

confidence: 99%

“…Similarly, the other entities (Objects Markers (OM), Discovery Service (DS), Control Service (CS), and Fusion Service (FS) in Figure ) in the DTS can follow this approach, and the trust of each subsystem can be investigated. Then the overall trust of the DTS ( T ( T S ) ) will be defined as,

T_{(TS)} = T_{(SS)} \cap T_{OM} \cap T_{DS} \cap T_{CS} \cap T_{FS} .

The earlier discussion includes a summary of a trust model – the authors' recent work provides more details of this approach.…”

Section: Application Of Trust Principles To the Case Study (Section 2)mentioning

confidence: 99%

Understanding the trust of software‐intensive distributed systems

Gallege

Gamage

Hill

et al. 2015

Concurrency and Computation

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Summary An early understanding of the trust concerns while composing a distributed system from independently developed software services saves time and effort. It also allows the developer of such distributed systems to reason about the trust‐related properties of these systems. Although there are prevalent approaches for evaluating the trust of such systems, it is not clear which approach, if any, is the most comprehensive and best suited for a given situation. Moreover, there is no agreement about a unified approach, for quantifying trust, which can be applied to the entire software life‐cycle of distributed systems. This article, first, motivates the need for such a quantification of trust via a case study from the domain of indoor tracking. It then provides a comprehensive survey of current approaches that define trust, in general domains, and then focuses on the relevant approaches from the domain of software‐oriented distributed systems. These prevalent efforts are categorized into groups using existing clustering tools and then are further analyzed for their comprehensiveness. The analysis depicts: (1) many trust‐related efforts and associated models have their own constrained views of trust; (2) different trust models focus on different aspects of trust and life‐cycle details; and (3) it is difficult to interoperate across different trust models. Hence, the paper identifies a set of principles that can assist in quantifying and evaluating the trust throughout the software life‐cycle of distributed systems. These principles, then, are applied to the aforementioned case study to provide an outline of how trustworthy distributed systems can be composed from independent software services. Copyright © 2015 John Wiley & Sons, Ltd.

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Towards trust-based recommender systems for online software services

Cited by 4 publications

References 7 publications

Credibility Analysis in Social Big Data

Credibility Analysis in Social Big Data

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Understanding the trust of software‐intensive distributed systems

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