Predicting Quality of Service via Leveraging Location Information
Joint Authors
Chen, Liang
Xie, Fenfang
Zheng, Zibin
Wu, Yaoming
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-11
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chen, Liang& Xie, Fenfang& Zheng, Zibin& Wu, Yaoming. 2019. Predicting Quality of Service via Leveraging Location Information. Complexity،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1131972
Modern Language Association (MLA)
Chen, Liang…[et al.]. Predicting Quality of Service via Leveraging Location Information. Complexity No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1131972
American Medical Association (AMA)
Chen, Liang& Xie, Fenfang& Zheng, Zibin& Wu, Yaoming. Predicting Quality of Service via Leveraging Location Information. Complexity. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1131972
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131972