A Survey on Web Service QoS Prediction Methods

Ghafouri, Seyyed Hamid; Hashemi, Seyyed Mohsen; Hung, Patrick C. K.

doi:10.1109/tsc.2020.2980793

Cited by 55 publications

(31 citation statements)

References 109 publications

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“…Although the above methods improve the accuracy of QoS prediction to some extent, they directly use the historical QoS data provided by all users, ignoring the existence of untrustworthy users in practice. Thus, one of the problems with CF-based method is invalid data [23]. For avoiding this problem, some researchers put forward a series of QoS prediction methods based on user reputation.…”

Section: Related Workmentioning

confidence: 99%

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QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

Wang

Zhang

et al. 2020

IEEE Access

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As the emergence of numerous services with similar functions, it is very helpful to recommend personalized services for users, and urgent to accurately predict the QoS(Quality-of-Service) values of Web services. Collaborative Filtering (CF) is a commonly-used method to handle above issues. However, it faces two common issues: data sparsity problem and trustworthiness issue, which greatly reduces its prediction accuracy. To address this problem properly and systematically, we introduce the network embedding learning into the QoS prediction process and propose an improved QoS prediction method based on the reputation-aware network embedding learning. Firstly, a two-phase K-means clustering is adopted to filter untrustworthy users. Next, the reputation of trustworthy users is calculated, and an attributed user-service bipartite network is constructed between trustworthy users and services while considering the user reputation. Then the reputation-aware network embedding is adopted to learn the hidden representations of users. Finally, user-based CF is adopted to predict the unknown QoS values. The experimental results show that our method has a significant improvement in accuracy compared with other methods.

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Section: Related Workmentioning

confidence: 99%

“…To resolve the trustworthiness issue and obtain accurate Web service QoS predictions, several methods have been proposed [12,23]. As described in the second section, they are very sensitive to parameter settings.…”

Section: A Two-phase K-means Clusteringmentioning

confidence: 99%

QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

Wang

Zhang

et al. 2020

IEEE Access

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“…In our previous study, we reviewed and classified the QoS prediction methods. According to this research, CF‐based methods are divided into two types: memory‐based and model‐based 20 . In memory‐based methods, the QoS values of each Web service are predicted for each user by extracting the similarity between users, or Web services, or both of them.…”

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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“…The flourish of web-based services gave rise to the research area called services computing, and its academic community has kept continuously expanding since nearly 20 years ago [1]. Services computing involves many classical research problems, such as service selection [2], service discovery [3], service composition [4], service classification [5], Quality of Service (QoS) prediction [6], and service recommendation [7].…”

Section: Introductionmentioning

confidence: 99%

External Service Sensing (ESS): Research Framework, Challenges and Opportunities

Wang,

Liu,

et al. 2021

Preprint

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The flourish of web-based services gave birth to the research area services computing, a rapidly-expanding academic community since nearly 20 years ago. Consensus has been reached on a set of representative research problems in services computing, such as service selection, service composition, service recommendation, and service quality prediction. An obvious fact is that most services keep constant changes to timely adapt to changes of external business/technical environment and changes of internal development strategies. However, traditional services computing research does not consider such changes sufficiently. Many works regard services as static entities; this leads to the situation that some proposed models/algorithms do not work in real world. Sensing various types of service changes is of great significance to the practicability and rationality of services computing research. In this paper, a new research problem External Service Sensing (ESS) is defined to cope with various changes in services, and a research framework of ESS is presented to elaborate the scope and boundary of ESS. This framework is composed of four orthogonal dimensions: sensing objects, sensing contents, sensing channels, and sensing techniques. Each concrete ESS problem is defined by combining different values in these dimensions, and existing research work related to service changes can be well adapted to this framework. Real-world case studies demonstrate the soundness of ESS and its framework. Finally, some challenges and opportunities in ESS research are listed for researchers in the services computing community. To the best of our knowledge, this is the first time to systematically define service change-related research as a standard services computing problem, and thus broadening the research scope of services computing.

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A Survey on Web Service QoS Prediction Methods

Cited by 55 publications

References 109 publications

QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

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

External Service Sensing (ESS): Research Framework, Challenges and Opportunities

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