Predicting Quality of Service via Leveraging Location Information
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 informatio...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/4932030 |
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| _version_ | 1832551899676540928 |
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| author | Liang Chen Fenfang Xie Zibin Zheng Yaoming Wu |
| author_facet | Liang Chen Fenfang Xie Zibin Zheng Yaoming Wu |
| author_sort | Liang Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ba7f1d0aecc243efbe11c5bb2323d609 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ba7f1d0aecc243efbe11c5bb2323d6092025-02-03T06:00:15ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/49320304932030Predicting Quality of Service via Leveraging Location InformationLiang Chen0Fenfang Xie1Zibin Zheng2Yaoming Wu3School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, ChinaQoS (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.http://dx.doi.org/10.1155/2019/4932030 |
| spellingShingle | Liang Chen Fenfang Xie Zibin Zheng Yaoming Wu Predicting Quality of Service via Leveraging Location Information Complexity |
| title | Predicting Quality of Service via Leveraging Location Information |
| title_full | Predicting Quality of Service via Leveraging Location Information |
| title_fullStr | Predicting Quality of Service via Leveraging Location Information |
| title_full_unstemmed | Predicting Quality of Service via Leveraging Location Information |
| title_short | Predicting Quality of Service via Leveraging Location Information |
| title_sort | predicting quality of service via leveraging location information |
| url | http://dx.doi.org/10.1155/2019/4932030 |
| work_keys_str_mv | AT liangchen predictingqualityofservicevialeveraginglocationinformation AT fenfangxie predictingqualityofservicevialeveraginglocationinformation AT zibinzheng predictingqualityofservicevialeveraginglocationinformation AT yaomingwu predictingqualityofservicevialeveraginglocationinformation |