QoS evaluation for web service recommendation

Ma, You; Xin, Xin; Wang, Shangguang; Li, Jinglin; Sun, Qibo; Yang, Fangchun

doi:10.1109/cc.2015.7114061

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…For example, Chen et al proposed an incremental tensor factorization method by considering the temporal information [10]. You et al proposed a method that models the high-dimensional QoS data as tensors with an important tensor operation for the purpose of predicting the unknown QoS property values [16].…”

Section: Collaborative Filtering-based Methodsmentioning

confidence: 99%

“…us, quality prediction for Web services has been an extremely active research field in recent years [11][12][13][14][15]. Among many prediction approaches, collaborative filtering has been the most popular and successful one for building recommendation systems [16][17][18]. Memory-based collaborative filtering, which uses known preferences of some users to predict preferences of unknown users, has been widely employed by many researchers for the quality prediction on Web services [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Quality Prediction of Web Services Based on a Covering Algorithm

2020

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Service-oriented architecture (SOA) is widely used, which has fueled the rapid growth of Web services and the deployment of tremendous Web services over the last decades. It becomes challenging but crucial to find the proper Web services because of the increasing amount of Web services. However, it proves unfeasible to inspect all the Web services to check their quality values since it will consume a lot of resources. Thus, developing effective and efficient approaches for predicting the quality values of Web services has become an important research issue. In this paper, we propose UIQPCA, a novel approach for hybrid User and Item-based Quality Prediction with Covering Algorithm. UIQPCA integrates information of both users and Web services on the basis of users’ ideas on the quality of coinvoked Web services. After the integration, users and Web services which are similar to the target user and the target Web service are selected. Then, considering the result of integration, UIQPCA makes predictions on how a target user will appraise a target Web service. Broad experiments on WS-Dream, a web service dataset which is widely used in real world, are conducted to evaluate the reliability of UIQPCA. According to the results of experiment, UIQPCA is far better than former approaches, including item-based, user-based, hybrid, and cluster-based approaches.

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Section: Collaborative Filtering-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality Prediction of Web Services Based on a Covering Algorithm

2020

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“…Ma et al [20] predicted QoS values by tensor decomposition, marked services by a user preference learning method, and implemented service recommendation using service scores.…”

Section: Research On Web Service Recommendationmentioning

confidence: 99%

An Efficient Knowledge-Graph-Based Web Service Recommendation Algorithm

et al. 2019

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Using semantic information can help to accurately find suitable services from a variety of available (different semantics) services, and the semantic information of Web services can be described in detail in a Web service knowledge graph. In this paper, a Web service recommendation algorithm based on knowledge graph representation learning (kg-WSR) is proposed. The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. By calculating the distance between service entities in low-dimensional space, the relationship information of services which is not considered in recommendation approaches using a collaborative filtering algorithm is incorporated into the recommendation algorithm to enhance the accurateness of the result. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate, and coverage rate of recommendation but also solve the cold start problem to some extent.

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“…With the rapid development of network and information technology, the network traffic classification has drawn plenty of attention. Network traffic classification is an essential factor for dynamic access control, network resources management, network survivability, quality of service (QoS) 1 . In fact, a mature network traffic classification makes efficient management of network and ensures precise resource allocation.…”

Section: Introductionmentioning

confidence: 99%

A novel method for network traffic classification based on robust support vector machine

Jie

Zhao

et al. 2020

Trans Emerging Tel Tech

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The increasing demand for high‐speed transmission and traffic classification has been widely applied for network resource management, quality of service, dynamic access control, and others. Some normal machine learning algorithms for network traffic classification are becoming obsolete, as the increasing deployment of encryption in protocols and dynamic ports, as well as some new technology are available, such as HTML5 protocols and HTTP adaptive streaming in video traffic. This article presents a novel classification algorithm for video traffic. We produce a new video traffic dataset and propose an original parameter adaptive algorithm for robust support vector machine with a fuzzy membership function. The results show that the proposed model is an effective alternative to be applied in actual network environment.

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QoS evaluation for web service recommendation

Cited by 13 publications

References 22 publications

Quality Prediction of Web Services Based on a Covering Algorithm

Quality Prediction of Web Services Based on a Covering Algorithm

An Efficient Knowledge-Graph-Based Web Service Recommendation Algorithm

A novel method for network traffic classification based on robust support vector machine

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