An Artificial Neural Network for Predicting Service Rating in the Presence of Rating Manipulation

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dc.contributor.author Zhao, P en
dc.contributor.author Ye, Xin en
dc.coverage.spatial New York en
dc.date.accessioned 2015-12-11T02:26:55Z en
dc.date.issued 2015 en
dc.identifier.citation Proceedings 2015 IEEE International Conference on Service Computing: SCC 2015, 2015, pp. 608 - 615 en
dc.identifier.isbn 978-1-4673-7281-7 en
dc.identifier.uri http://hdl.handle.net/2292/27723 en
dc.description.abstract Accurately predicting a user’s rating to a service is a challenging task in the presence of malicious users that manipulate the ratings to services. Many existing service rating systems lack the ability that counter the manipulation of rating systems. This paper proposed an artificial neural network (ANN) based service rating scheme that counters the manipulation of service ratings. The scheme takes into account of both similarity-based rating and the ratings given by representative users when predicting a user’s rating to a service. Some experiments were carried out to compare the prediction accuracy of the proposed scheme with a well-known existing scheme WSRec [26]. The results show that the proposed scheme provides more accurate rating predictions in the presence of a large amount of malicious users. en
dc.relation.ispartof 12th IEEE International Conference on Service Computing en
dc.relation.ispartofseries Proceedings 2015 IEEE International Conference on Service Computing: SCC 2015 en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.ieee.org/publications_standards/publications/rights/rights_policies.html en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title An Artificial Neural Network for Predicting Service Rating in the Presence of Rating Manipulation en
dc.type Conference Item en
dc.identifier.doi 10.1109/SCC.2015.88 en
pubs.begin-page 608 en
dc.description.version AM - Accepted Manuscript en
pubs.end-page 615 en
pubs.finish-date 2015-07-02 en
pubs.start-date 2015-06-27 en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Proceedings en
pubs.elements-id 506281 en
pubs.org-id Faculty of Science en
pubs.org-id Computer Science en
pubs.record-created-at-source-date 2015-11-25 en


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