Contexts in a paper recommendation system with collaborative filtering

Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users’ interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier exp...

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Autores principales:	Pinata Winoto, Tiffany Y. Tang, Gordon I. McCalla
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2012
Materias:	e-learning pedagogy Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/9a66cd6547bc489688b025638257a3e3
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id	oai:doaj.org-article:9a66cd6547bc489688b025638257a3e3
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spelling	oai:doaj.org-article:9a66cd6547bc489688b025638257a3e32021-12-02T19:20:49ZContexts in a paper recommendation system with collaborative filtering10.19173/irrodl.v13i5.12431492-3831https://doaj.org/article/9a66cd6547bc489688b025638257a3e32012-11-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/1243https://doaj.org/toc/1492-3831Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users’ interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students demonstrated the significance of this approach using modified collaborative filtering techniques. However, two key issues remain: (1) How would the modified filtering perform when target users are inexperienced undergraduate students who have a different pedagogical background and contextual information-seeking goals, such as task- and course-related goals, from those of graduate students?; (2) Should we combine graduates and undergraduates in the same pool, or should we separate them? We conducted two studies aimed at addressing these issues and they showed that (1) the system can be effectively used for inexperienced learners; (2) recommendations are less effective for different learning groups (with different pedagogical features and learning goals) than they are for the same learning groups. Based on the results obtained from these studies, we suggest several context-aware filtering techniques for different learning scenarios. Pinata WinotoTiffany Y. TangGordon I. McCallaAthabasca University Pressarticlee-learningpedagogySpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 13, Iss 5 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	e-learning pedagogy Special aspects of education LC8-6691
spellingShingle	e-learning pedagogy Special aspects of education LC8-6691 Pinata Winoto Tiffany Y. Tang Gordon I. McCalla Contexts in a paper recommendation system with collaborative filtering
description	Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users’ interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students demonstrated the significance of this approach using modified collaborative filtering techniques. However, two key issues remain: (1) How would the modified filtering perform when target users are inexperienced undergraduate students who have a different pedagogical background and contextual information-seeking goals, such as task- and course-related goals, from those of graduate students?; (2) Should we combine graduates and undergraduates in the same pool, or should we separate them? We conducted two studies aimed at addressing these issues and they showed that (1) the system can be effectively used for inexperienced learners; (2) recommendations are less effective for different learning groups (with different pedagogical features and learning goals) than they are for the same learning groups. Based on the results obtained from these studies, we suggest several context-aware filtering techniques for different learning scenarios.
format	article
author	Pinata Winoto Tiffany Y. Tang Gordon I. McCalla
author_facet	Pinata Winoto Tiffany Y. Tang Gordon I. McCalla
author_sort	Pinata Winoto
title	Contexts in a paper recommendation system with collaborative filtering
title_short	Contexts in a paper recommendation system with collaborative filtering
title_full	Contexts in a paper recommendation system with collaborative filtering
title_fullStr	Contexts in a paper recommendation system with collaborative filtering
title_full_unstemmed	Contexts in a paper recommendation system with collaborative filtering
title_sort	contexts in a paper recommendation system with collaborative filtering
publisher	Athabasca University Press
publishDate	2012
url	https://doaj.org/article/9a66cd6547bc489688b025638257a3e3
work_keys_str_mv	AT pinatawinoto contextsinapaperrecommendationsystemwithcollaborativefiltering AT tiffanyytang contextsinapaperrecommendationsystemwithcollaborativefiltering AT gordonimccalla contextsinapaperrecommendationsystemwithcollaborativefiltering
_version_	1718376809970532352

Contexts in a paper recommendation system with collaborative filtering

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