CAHPHF: Context-Aware Hierarchical QoS Prediction with Hybrid Filtering

Roy Chowdhury, R; Chattopadhyay, S; Adak, C

CAHPHF: Context-Aware Hierarchical QoS Prediction with Hybrid Filtering

Roy Chowdhury, R Chattopadhyay, S Adak, C

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Services Computing, 2022, 15, (4), pp. 2232-2247
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Roy Chowdhury, R
dc.contributor.author	Chattopadhyay, S
dc.contributor.author	Adak, C https://orcid.org/0000-0002-9085-2770
dc.date.accessioned	2023-03-20T22:27:52Z
dc.date.available	2023-03-20T22:27:52Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Services Computing, 2022, 15, (4), pp. 2232-2247
dc.identifier.issn	1939-1374
dc.identifier.issn	1939-1374
dc.identifier.uri	http://hdl.handle.net/10453/167808
dc.description.abstract	IEEE With the proliferation of Internet-of-Things and continuous growth in the number of web-services at the Internet-scale, service-recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service-recommendation is the Quality-of-Service(QoS) parameter, which depicts the performance of a web-service. In general, the service provider furnishes the QoS values before service deployment. In reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task. Thus, QoS-prediction has gained significant attention. Multiple approaches are available in the literature for predicting QoS. However, these approaches are yet to reach the desired accuracy level. Here, we study the QoS-prediction problem across different users and propose a novel solution by considering the contextual information of both services and users. Our proposal includes two key-steps: (a)hybrid-filtering, (b)hierarchical-prediction-mechanism. On one hand, the hybrid-filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical-prediction-mechanism is to estimate the QoS value accurately by leveraging hierarchical-neural-regression. We evaluated our framework on WS-DREAM datasets. The experimental results show our framework outperformed the major state-of-the-art approaches.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Services Computing
dc.relation.isbasedon	10.1109/TSC.2020.3041626
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems
dc.title	CAHPHF: Context-Aware Hierarchical QoS Prediction with Hybrid Filtering
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-20T22:27:50Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	4

Abstract:

IEEE With the proliferation of Internet-of-Things and continuous growth in the number of web-services at the Internet-scale, service-recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service-recommendation is the Quality-of-Service(QoS) parameter, which depicts the performance of a web-service. In general, the service provider furnishes the QoS values before service deployment. In reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task. Thus, QoS-prediction has gained significant attention. Multiple approaches are available in the literature for predicting QoS. However, these approaches are yet to reach the desired accuracy level. Here, we study the QoS-prediction problem across different users and propose a novel solution by considering the contextual information of both services and users. Our proposal includes two key-steps: (a)hybrid-filtering, (b)hierarchical-prediction-mechanism. On one hand, the hybrid-filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical-prediction-mechanism is to estimate the QoS value accurately by leveraging hierarchical-neural-regression. We evaluated our framework on WS-DREAM datasets. The experimental results show our framework outperformed the major state-of-the-art approaches.

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http://hdl.handle.net/10453/167808