Alleviating Data Sparsity in Web Service QoS Prediction by Capturing Region Context Influence

Chen, Zhen; Shen, Limin; You, Dianlong; Li, Feng; Ma, Chuan

doi:10.1007/978-3-319-59288-6_53

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…This method has used the geographic information of services and users for clustering and then combined this information with MF to gain more accuracy in prediction 40 . Colbar, LMF‐PP, and REMF are other prediction methods that have used the location information for more accurate predictions 13,16,41,42 . Therefore, using the location information of the users can improve the accuracy of the predictions, because the users located in the same geographical location have similar QoS values.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

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

Ghafouri

Hashemi

Razzazi

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…However, QoS information is influenced by network and geological factors. As Internet is dynamic and vulnerable, it is not possible to get the same QoS values for different users from diverse locations for a particular service [17,18].…”

Section: Introductionmentioning

confidence: 99%

Web service QoS prediction using improved software source code metrics

2020

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Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services' QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics.

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Context and auto-interaction are all you need: Towards context embedding based QoS prediction via automatic feature interaction for high quality cloud API delivery

Chen

Pan

et al. 2022

Future Generation Computer Systems

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Alleviating Data Sparsity in Web Service QoS Prediction by Capturing Region Context Influence

Cited by 4 publications

References 13 publications

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

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

Web service QoS prediction using improved software source code metrics

Context and auto-interaction are all you need: Towards context embedding based QoS prediction via automatic feature interaction for high quality cloud API delivery

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