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Exploiting scholar's background knowledge to improve recommender system for digital libraries
Abstract Recommender systems 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'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.vement in accuracy of recommendation task.
Abstractsub Recommender systems 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'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.vement in accuracy of recommendation task.
Bibtextype article  +
Doi 10.4156/jdcta.vol6.issue22.12  +
Has author Amini B. + , Ibrahim R. + , Othman M.S. +
Has extra keyword Background knowledge + , Personalizations + , Recommendation accuracy + , Research studies + , Scientific articles + , Semantic concept + , User interests + , User profile + , Wikipedia + , Digital libraries + , Knowledge based systems + , Ontology + , Semantics + , Recommender system +
Has keyword Background knowledge + , Digital libraries + , Ontology + , Recommender system + , User profile +
Issn 19759339  +
Issue 22  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 119–128  +
Published in International Journal of Digital Content Technology and its Applications +
Title Exploiting scholar's background knowledge to improve recommender system for digital libraries +
Type journal article  +
Volume 6  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 7 November 2014 18:00:44  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 18:00:44  +
DateThis property is a special property in this wiki. 2012  +
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