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Exploiting scholar's background knowledge to improve recommender system for digital libraries

Recommender systems for digital libraries have received increasing attention since they assist scholars to find the most appropriate articles for research purposes. Many research studies have recently conducted to model the user interests in order to suggest scientific articles based on the scholar...

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Main Authors: Amini, Bahram, Ibrahim, Roliana, Othman, Mohd. Shahizan
Format: Article
Published: 2012
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/46965/
http://dx.doi.org/10.4156/jdcta.vol6.issue22.12
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id my.utm.46965
record_format eprints
spelling my.utm.469652017-09-27T05:01:52Z http://eprints.utm.my/id/eprint/46965/ Exploiting scholar's background knowledge to improve recommender system for digital libraries Amini, Bahram Ibrahim, Roliana Othman, Mohd. Shahizan TK Electrical engineering. Electronics Nuclear engineering Recommender systems for digital libraries have received increasing attention since they assist scholars to find the most appropriate articles for research purposes. Many research studies have recently conducted to model the user interests in order to suggest scientific articles based on the scholar’s preferences. However, a major problem of such systems is that they do not subsume user’s background knowledge into the recommendation process and scholars typically have to sift manually irrelevant articles retrieved from digital libraries. Therefore, a challenging task is how to collect and exploit sufficient scholar’s academic knowledge into the personalization process in order to improve the recommendation accuracy. To address this problem, a recommender framework that consolidates scholar’s background knowledge based on the ontological modeling is proposed. The framework exploits Wikipedia as a lexicographic database for concept disambiguation and semantic concept mapping. The practical evaluation by a group of scholars over CiteSeerX digital library indicates an improvement in accuracy of recommendation task. 2012 Article PeerReviewed Amini, Bahram and Ibrahim, Roliana and Othman, Mohd. Shahizan (2012) Exploiting scholar's background knowledge to improve recommender system for digital libraries. International Journal of Digital Content Technology and its Applications, 6 (22). pp. 119-128. ISSN 1975-9339 http://dx.doi.org/10.4156/jdcta.vol6.issue22.12
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Amini, Bahram
Ibrahim, Roliana
Othman, Mohd. Shahizan
Exploiting scholar's background knowledge to improve recommender system for digital libraries
description Recommender systems for digital libraries have received increasing attention since they assist scholars to find the most appropriate articles for research purposes. Many research studies have recently conducted to model the user interests in order to suggest scientific articles based on the scholar’s preferences. However, a major problem of such systems is that they do not subsume user’s background knowledge into the recommendation process and scholars typically have to sift manually irrelevant articles retrieved from digital libraries. Therefore, a challenging task is how to collect and exploit sufficient scholar’s academic knowledge into the personalization process in order to improve the recommendation accuracy. To address this problem, a recommender framework that consolidates scholar’s background knowledge based on the ontological modeling is proposed. The framework exploits Wikipedia as a lexicographic database for concept disambiguation and semantic concept mapping. The practical evaluation by a group of scholars over CiteSeerX digital library indicates an improvement in accuracy of recommendation task.
format Article
author Amini, Bahram
Ibrahim, Roliana
Othman, Mohd. Shahizan
author_facet Amini, Bahram
Ibrahim, Roliana
Othman, Mohd. Shahizan
author_sort Amini, Bahram
title Exploiting scholar's background knowledge to improve recommender system for digital libraries
title_short Exploiting scholar's background knowledge to improve recommender system for digital libraries
title_full Exploiting scholar's background knowledge to improve recommender system for digital libraries
title_fullStr Exploiting scholar's background knowledge to improve recommender system for digital libraries
title_full_unstemmed Exploiting scholar's background knowledge to improve recommender system for digital libraries
title_sort exploiting scholar's background knowledge to improve recommender system for digital libraries
publishDate 2012
url http://eprints.utm.my/id/eprint/46965/
http://dx.doi.org/10.4156/jdcta.vol6.issue22.12
_version_ 1643652194237415424
score 13.211869

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