QoS prediction for web services based on user-trust propagation model

Thinh, Le Van; Truong, Dinh-Tu

doi:10.1080/13614568.2017.1416681

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Some users also may contribute random values for QoS attributes 32 . Therefore, some papers have proposed trust‐aware methods that validate these data before using them 2,5,33,34 . These methods achieved more reliable predictions by eliminating untrusted data or reducing the impact of them on prediction.…”

Section: Introductionmentioning

confidence: 99%

Web service quality of service prediction via regional reputation‐based matrix factorization

Ghafouri

Hashemi

Razzazi

et al. 2021

Concurrency and Computation

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Quality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to predict these values. CF‐based methods use the QoS values contributed by the other users for prediction and, consequently, the values contributed by unreliable users can decrease the accuracy of prediction. To utilize the reputation of users can be regarded as one of the conventional approaches to overcome this problem. In this paper, we have defined a concept called regional reputation that represents the reputation of a user for users in each geographical region. Regional reputation has been achieved with the combination of the location information of the users and their reputation. Subsequently, by combining this concept with the matrix factorization, we have proposed a prediction method called regional reputation‐based matrix factorization. This approach has been able to improve the accuracy of prediction and be more persistent to the data contributed by unreliable users.

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Section: Introductionmentioning

confidence: 99%