Web service reputation prediction based on customer feedback forecasting model

Al-Sharawneh, J; Williams, MA; Goldbaum, D

Web service reputation prediction based on customer feedback forecasting model

Al-Sharawneh, J Williams, MA

Goldbaum, D

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOC, 2010, pp. 33 - 40
Issue Date:: 2010-12-01

Closed Access

	Filename	Description	Size
	2010000121OK.pdf		1.53 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Al-Sharawneh, J	en_US
dc.contributor.author	Williams, MA https://orcid.org/0000-0002-1047-0503	en_US
dc.contributor.author	Goldbaum, D https://orcid.org/0000-0002-4228-1408	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOC, 2010, pp. 33 - 40	en_US
dc.identifier.isbn	9780769541648	en_US
dc.identifier.issn	1541-7719	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16212
dc.description.abstract	In the Service Web, customers' feedback constitutes a substantial component of Web Service reputation and trustworthiness, which in turn impacts the service uptake by consumers in the future. This paper presents an approach to predict reputation in service-oriented environments. For assessing a Web Service reputation, we define reputation key metrics to aggregate the feedback of different aspects of the ratings. In situations where rating feedback is not available, we propose a Feedback Forecasting Model (FFM), based on Expectation Disconfirmation Theory (EDT), to predict the reputation of a web service in dynamic settings. Then we introduce the concept "Reputation Aspect" and show how to compute it efficiently. Finally we show how to integrate the Feedback Forecasting Model into Aspect-Based Reputation Computation. To demonstrate the feasibility and effectiveness of our approach, we test the proposed model using our Service Selection Simulation Studio (4S). The simulation results included in this paper show the applicability and performance of the proposed Reputation Prediction based on the Customer Feedback Forecasting Model. We also show how our model is efficient, particularly in dynamic environments. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOC	en_US
dc.relation.isbasedon	10.1109/EDOCW.2010.16	en_US
dc.title	Web service reputation prediction based on customer feedback forecasting model	en_US
dc.type	Conference Proceeding
utslib.for	080612 Interorganisational Information Systems and Web Services	en_US
dc.location.activity	Brazil	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In the Service Web, customers' feedback constitutes a substantial component of Web Service reputation and trustworthiness, which in turn impacts the service uptake by consumers in the future. This paper presents an approach to predict reputation in service-oriented environments. For assessing a Web Service reputation, we define reputation key metrics to aggregate the feedback of different aspects of the ratings. In situations where rating feedback is not available, we propose a Feedback Forecasting Model (FFM), based on Expectation Disconfirmation Theory (EDT), to predict the reputation of a web service in dynamic settings. Then we introduce the concept "Reputation Aspect" and show how to compute it efficiently. Finally we show how to integrate the Feedback Forecasting Model into Aspect-Based Reputation Computation. To demonstrate the feasibility and effectiveness of our approach, we test the proposed model using our Service Selection Simulation Studio (4S). The simulation results included in this paper show the applicability and performance of the proposed Reputation Prediction based on the Customer Feedback Forecasting Model. We also show how our model is efficient, particularly in dynamic environments. © 2010 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16212