Estimating the reputation of newcomer web services using a regression-Based method

Tibermacine, Okba; Tibermacine, Chouki; Cherif, Foudil

doi:10.1016/j.jss.2018.08.026

Cited by 13 publications

(12 citation statements)

References 66 publications

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“…In a recently published work, Tibermacine et al [47] proposed a method to determine the reputation of similar web services. Researchers have employed the application of support vector regression algorithm to estimate the unknown QoS values of web services from their known values.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

et al. 2020

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To accurately rank various web services can be a very challenging task depending on the evaluation criteria used, however, it can play an important role in performing a better selection of web services afterward. This paper proposes an approach to evaluate trust prediction and confusion matrix to rank web services from throughput and response time. AdaBoostM1 and J48 classifiers are used as binary classifiers on a benchmark web services dataset. The trust score (TS) measuring method is proposed by using the confusion matrix to determine trust scores of all web services. Trust prediction is calculated using 5-Fold, 10-Fold, and 15-Fold cross-validation methods. The reported results showed that the web service 1 (WS1) was most trusted with (48.5294%) TS value, and web service 2 (WS2) was least trusted with (24.0196%) TS value by users. Correct prediction of trusted and untrusted users in web services invocation has improved the overall selection process in a pool of similar web services. Kappa statistics values are used for the evaluation of the proposed approach and for performance comparison of the two above-mentioned classifiers.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

et al. 2020

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“…Okab et al [9] combined QoS attributes with reputation values of similar services to estimate the reputation values of newcomer services based on regression models. Wu et al [6] presented a reputation bootstrapping approach, where correlations between the QoS and reputation performance of existing services are first learned through artificial neural networks and then generalized to determine a reputation value of new and unknown services.…”

Section: Related Workmentioning

confidence: 99%

“…For example, if OqVal = 4, fair feedback ratings should belong to 3-5. A deviation of ±1 from OqVal represents natural variation, and malicious users give ratings outside this interval [9]. Additionally, the value of the factor ''after-sale service'' has to be determined for new vendors.…”

Section: B Data Preparation and Descriptionmentioning

confidence: 99%

“…As far as we know, only a few researchers have attempted to handle the reputation bootstrapping problem without setting a default initial value [6], [9], [10]. The basic idea of most reputation bootstrapping models is that the correlations between quality of service (QoS) features and the performances of existing vendors are first learned through samples and then generalized to acquire the reputation values of new vendors.…”

Section: Introductionmentioning

confidence: 99%

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A Reputation Bootstrapping Model for E-Commerce Based on Fuzzy DEMATEL Method and Neural Network

2019

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Reputation evaluation plays an important role in determining the credibility of online entities. Especially in e-commerce systems, consumers usually give priority to this indicator when choosing vendors. Reputation bootstrapping, which can determine the default reputation value of new entities, is still a challenging problem. Most current bootstrapping methods fail to consider the complexity, the ambiguity of trust, or the reputation and internal correlation characteristics of influential factors in the prediction process. Therefore, in this paper, a novel reputation bootstrapping model that combines the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method with neural network prediction is established. First, we adopt a fuzzy multi-criteria decision-making model, named fuzzy DEMATEL method to discuss the intrinsic causal relationships between influential factors and identify the critical success factors (CSFs) for reputation estimation. Then, we adopt back propagation (BP) neural network to generalize the correlations between the CSFs and the initial reputation value. Finally, a case study is constructed to verify the proposed model. The experimental results indicate that the proposed model has better accuracy and efficiency compared with other reputation bootstrapping methods.

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“…To do so, as several web service reputation management approaches (e.g. [6], [15], [16]), we have simulated the interactions between a set of 339 users and the 409 web services. At the end of the interaction, for each user, we generate feedback values for its consumed Web service.…”

Section: Feedback Rating Simulationmentioning

confidence: 99%

Reputation Evaluation with Malicious Feedback Prevention Using a HITS-Based Model

Tibermacine¹,

Tibermacine²,

Kerdoudi

2019

2019 IEEE International Conference on Web Services (ICWS)

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The reputation of web services is calculated by aggregating user feedback ratings. Though reputation is a subjective metric, it can be considered as a good indicator about services Quality of Experience, and henceforth, it can be used for recommending services in an open ecosystem. In this work, we propose a three-phase process for evaluating web service reputation by aggregating user feedback ratings. The relationship between users and services is modeled as a bipartite graph where an adapted HITS (Hypertext Induced Topic Search) algorithm is employed to distinguish between honest and malicious users in Phase I. Then, this model is used to evaluate, in Phase III, the reputation of web services from user ratings after punishing malicious users in Phase II. An experiment on a dataset of real Web services was conducted to validate the effectiveness of the proposed model in evaluating Web service reputation.

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Estimating the reputation of newcomer web services using a regression-Based method

Cited by 13 publications

References 66 publications

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

A Reputation Bootstrapping Model for E-Commerce Based on Fuzzy DEMATEL Method and Neural Network

Reputation Evaluation with Malicious Feedback Prevention Using a HITS-Based Model

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