Trust In A Hybrid Recommender Systems

In this research,a novel trust calculation that is incorporated into a hybrid recommender system is proposed in order to increase the accuracy of rating prediction. The accuracy of the proposed system was measured based on Recall and Precision.

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Bibliographic Details
Main Author:	Karimi, Morteza
Format:	Thesis
Published:	2011
Subjects:	QA76.75-76.765 Computer software
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id	my-mmu-ep.3649
record_format	uketd_dc
spelling	my-mmu-ep.36492012-11-27T06:59:13Z Trust In A Hybrid Recommender Systems 2011-10 Karimi, Morteza QA76.75-76.765 Computer software In this research,a novel trust calculation that is incorporated into a hybrid recommender system is proposed in order to increase the accuracy of rating prediction. The accuracy of the proposed system was measured based on Recall and Precision. 2011-10 Thesis http://shdl.mmu.edu.my/3649/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php masters Multimedia University Faculty of Information Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA76.75-76.765 Computer software
spellingShingle	QA76.75-76.765 Computer software Karimi, Morteza Trust In A Hybrid Recommender Systems
description	In this research,a novel trust calculation that is incorporated into a hybrid recommender system is proposed in order to increase the accuracy of rating prediction. The accuracy of the proposed system was measured based on Recall and Precision.
format	Thesis
qualification_level	Master's degree
author	Karimi, Morteza
author_facet	Karimi, Morteza
author_sort	Karimi, Morteza
title	Trust In A Hybrid Recommender Systems
title_short	Trust In A Hybrid Recommender Systems
title_full	Trust In A Hybrid Recommender Systems
title_fullStr	Trust In A Hybrid Recommender Systems
title_full_unstemmed	Trust In A Hybrid Recommender Systems
title_sort	trust in a hybrid recommender systems
granting_institution	Multimedia University
granting_department	Faculty of Information Technology
publishDate	2011
_version_	1747829534985551872

Trust In A Hybrid Recommender Systems

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