Collaborative QoS prediction with context-sensitive matrix factorization

Wu, Hao; Yue, Kun; Li, Bo; Zhang, Binbin; Hsu, Ching‐Hsien

doi:10.1016/j.future.2017.06.020

Cited by 106 publications

(38 citation statements)

References 20 publications

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“…The location information can help choose similar neighbors for the objective user or the objective service. Wu et al [7] used the invocation record between users and services, and a generalized context-sensitive matrix factorization method was proposed to predict QoS values for services. Kuang et al [8] took advantage of the user's reputation and the user's and the service's location information, and a personalized QoS prediction method was proposed to solve the problem of data sparsity, cold start, and data incredibility.…”

Section: Location-aware Qos Prediction Users' and Web Services'mentioning

confidence: 99%

“…= − ⋅ (̂− )̂ (7) where is the model parameters (e.g., , V ), > 0 is the learning rate, and controls the speed of gradient descent.…”

Section: Model Learningmentioning

confidence: 99%

“…Then, it uses factorization machine to predict QoS for users (6) RegionKNN (region K nearest neighbors) [27]: this approach firstly clusters users and services on the basis of the location information and QoS values. Then it employs the clustering results to predict QoS for users (7) LACF (location-aware collaborative filtering) [12]: this approach incorporates users' and services' locations and employs a location-aware collaborative filtering approach to predict QoS for users (8) LBR (location-based regularization) [28]: this approach uses the local connectivity between users and combines regularization terms in classical matrix factorization framework to predict QoS for users (9) HMF (hierarchical matrix factorization) [29]: this approach firstly groups users and services according to the location information. Then, it unifies the results from local matrix factorization and global matrix factorization to predict QoS for users…”

Section: Performance Comparisonmentioning

confidence: 99%

“…Thus, taking location factors into account is useful to improve the QoS prediction results. Recently, a few works [7][8][9][10][11][12] have noticed the effect of users' location on QoS values. These investigations are mainly based on the observation that when invoking the same Web service, users in different places may go through distinct experience due to the diverse physical infrastructure.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Quality of Service via Leveraging Location Information

et al. 2019

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QoS (Quality of Service) (our approach can be applied to a wide variety of services; in this paper, we focus on Web services) performance is intensively relevant to locations due to the network distance and the Internet connection between users and services. Thus, considering the location information of services and users is necessary. However, the location information has been ignored by most previous work. In this paper, we take both services’ and users’ location information into account. Specifically, we propose a location-aware QoS prediction approach, called LANFM, by exploiting neural network techniques and factorization machine to improve user-perceived experience. First of all, the information (e.g., id and location) of services and users is expressed as embedding vectors by leveraging neural network techniques. Then, the inner product of various embedding vectors, along with the weighted sum of feature vectors, is used to predict the QoS values. It should be noted that the inner product operation could capture the interactions between services and users, which is helpful to predict QoS values of services that have not been invoked by users. A collection of extensive experiments have been carried out on a real-world dataset to validate the effectiveness of the LANFM model.

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Section: Location-aware Qos Prediction Users' and Web Services'mentioning

confidence: 99%

“…= − ⋅ (̂− )̂ (7) where is the model parameters (e.g., , V ), > 0 is the learning rate, and controls the speed of gradient descent.…”

Section: Model Learningmentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Quality of Service via Leveraging Location Information

et al. 2019

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“…Alternatively, the webserver is also a source of QoS values [8], but providers' consent is mandatory before obtaining the metrics values. Similar to recent works [9], [10], we also use the QoS values collected at the client-side. Many of the primary studies used quality metrics to forecast the quality of web services.…”

Section: Introductionmentioning

confidence: 99%

Benchmark Dataset Selection of Web Services Technologies: A Factor Analysis

et al. 2020

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Web services have emerged as an accessible technology with the standard 'Extensible Mark Up' (XML) language, which is known as 'Web Services Description Language' WSDL. Web services have become a promising technology to promote the interrelationship between service providers and users. Web services users' trust is measured by quality metrics. Web service quality metrics vary in many benchmark datasets used in the existing studies. The selection of a benchmark dataset is problematic to classify and retest web services. This paper proposes a method to rank web services quality metrics for the selection of benchmark web services datasets. To measure the diversity in quality metrics, factor analysis with Varimax rotation and scree plot is a well-established method. We use factor analysis to determine percentage variance among principal factors of four benchmark datasets. Our results showed that the two-factor solution explained 94.501, 76.524, and 45.009% variances in datasets A, B, and D, respectively. A three-factor solution explained 85.085% variance in dataset C. Reliability, and response time quality metrics were predicted as the most dominating quality metrics that contributed to explain the percentage variance in four datasets. Our proposed web metric ranking (WMR) method resulted in reliability as the topmost web metric with (57.62%) score and latency web metric at the bottom-most with (3.60%) score. The proposed WMR method showed a high (96.17%) ranking precision. Obtained results verified that factor solutions after reducing the dimensions could be generalized and used in the quality improvement of web services. In future works, the authors plan to focus on a dataset with dominating quality metrics to perform regression testing of web services. INDEX TERMS Factor analysis, quality metrics, rotated loading, reliability, response time, regression testing, web services.

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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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Collaborative QoS prediction with context-sensitive matrix factorization

Cited by 106 publications

References 20 publications

Predicting Quality of Service via Leveraging Location Information

Predicting Quality of Service via Leveraging Location Information

Benchmark Dataset Selection of Web Services Technologies: A Factor Analysis

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

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