Evaluating algorithms for an expertise recommender system

This study evaluates two algorithms, Support Vector Machine and Bayesian Logistic Regression as applied to an Expertise Recommender System, and compares the results. The expertise area examined is academic research, the information being extracted from faculty web pages of universities world-wide. T...

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Bibliographic Details
Main Author:	Uma Manikantan
Other Authors:	Goh, Dion Hoe Lian
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Library and information science::Libraries::Technologies
Online Access:	http://hdl.handle.net/10356/1907
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-1907
record_format	dspace
spelling	sg-ntu-dr.10356-19072019-12-10T14:46:29Z Evaluating algorithms for an expertise recommender system Uma Manikantan Goh, Dion Hoe Lian Wee Kim Wee School of Communication and Information DRNTU::Library and information science::Libraries::Technologies This study evaluates two algorithms, Support Vector Machine and Bayesian Logistic Regression as applied to an Expertise Recommender System, and compares the results. The expertise area examined is academic research, the information being extracted from faculty web pages of universities world-wide. The study is aimed at the ability of distinguishing subject specialisations within a main subject. Master of Science (Information Studies) 2008-09-10T08:37:16Z 2008-09-10T08:37:16Z 2004 2004 Thesis http://hdl.handle.net/10356/1907 Nanyang Technological University application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
topic	DRNTU::Library and information science::Libraries::Technologies
spellingShingle	DRNTU::Library and information science::Libraries::Technologies Uma Manikantan Evaluating algorithms for an expertise recommender system
description	This study evaluates two algorithms, Support Vector Machine and Bayesian Logistic Regression as applied to an Expertise Recommender System, and compares the results. The expertise area examined is academic research, the information being extracted from faculty web pages of universities world-wide. The study is aimed at the ability of distinguishing subject specialisations within a main subject.
author2	Goh, Dion Hoe Lian
author_facet	Goh, Dion Hoe Lian Uma Manikantan
format	Theses and Dissertations
author	Uma Manikantan
author_sort	Uma Manikantan
title	Evaluating algorithms for an expertise recommender system
title_short	Evaluating algorithms for an expertise recommender system
title_full	Evaluating algorithms for an expertise recommender system
title_fullStr	Evaluating algorithms for an expertise recommender system
title_full_unstemmed	Evaluating algorithms for an expertise recommender system
title_sort	evaluating algorithms for an expertise recommender system
publishDate	2008
url	http://hdl.handle.net/10356/1907
_version_	1681035336775368704

Evaluating algorithms for an expertise recommender system

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