An intelligent mechanism for adaptive peer user modeling in web-based environments

Giannoukos, I; Lykourentzou, I; Mpardis, G; Nikolopoulos, V; Loumos, V; Kayafas, E

dc.contributor.author	Giannoukos, I	en
dc.contributor.author	Lykourentzou, I	en
dc.contributor.author	Mpardis, G	en
dc.contributor.author	Nikolopoulos, V	en
dc.contributor.author	Loumos, V	en
dc.contributor.author	Kayafas, E	en
dc.date.accessioned	2014-03-01T02:45:06Z
dc.date.available	2014-03-01T02:45:06Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32157
dc.subject	Feed Forward Neural Network	en
dc.subject	Peer Assessment	en
dc.subject	User Model	en
dc.subject.other	E-learning	en
dc.subject.other	Information theory	en
dc.subject.other	Intelligent control	en
dc.subject.other	Internet	en
dc.subject.other	Multimedia systems	en
dc.subject.other	Semantics	en
dc.subject.other	Feed-forward neural networks	en
dc.subject.other	Intelligent mechanisms	en
dc.subject.other	Novel techniques	en
dc.subject.other	Peer assessments	en
dc.subject.other	User Modeling	en
dc.subject.other	Web-based environments	en
dc.subject.other	Neural networks	en
dc.title	An intelligent mechanism for adaptive peer user modeling in web-based environments	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/SMAP.2008.33	en
heal.identifier.secondary	http://dx.doi.org/10.1109/SMAP.2008.33	en
heal.identifier.secondary	4724869	en
heal.publicationDate	2008	en
heal.abstract	Peer assessment techniques are an effective means to take advantage of the knowledge that exists in webbased peer environments. Through these techniques, participants act both as authors and reviewers over each other's work. However, as web-based cooperating environments continuously grow in popularity, there is a need to develop intelligent mechanisms that will retrieve the optimal group of reviewers to comment on the work of each author, with a view to increasing the usefulness that these comments will have on the author's final result. This paper introduces a novel technique that incorporates feed forward neural networks to determine the optimal reviewers for a specific author during a peer assessment procedure. The proposed method seeks to match author to reviewer profiles based on feedback regarding the usefulness of reviewer comments as it was perceived by the author. The method was tested on educational e-learning data and the preliminary results that it yielded are promising. © 2008 IEEE.	en
heal.journalName	Proceedings - 3rd International Workshop on Semantic Media Adaptation and Personalization, SMAP 2008	en
dc.identifier.doi	10.1109/SMAP.2008.33	en
dc.identifier.spage	177	en
dc.identifier.epage	182	en