File Download
Supplementary

Article: Book Recommendation System using Data Mining for the University of Hong Kong Libraries

TitleBook Recommendation System using Data Mining for the University of Hong Kong Libraries
Authors
KeywordsRecommender Systems
OPAC
Subject searching
Data Mining
Library Technology
Issue Date2012
PublisherCentre for Information Technology in Education, Faculty of Education, University of Hong Kong. The Journal's web site is located at http://ejournal.cite.hku.hk/
Citation
Information, Technology and Educational Change, 2012 How to Cite?
AbstractThis paper describes the theoretical design of a Library Recommendation System, employing k- means clustering Data Mining algorithm, with subject headings of borrowed items as the basis for generating pertinent recommendations. Sample data from the University of Hong Kong Libraries (HKUL) has been used in a Quantitative approach to study the existing Library Information System, Innopac. Data Warehousing and Data Mining (k-means clustering) techniques are discussed. The primary benefit of the system is higher quality of academic research ensuing from better search results. Personalization improves individual effectiveness of learners and overall in better utilizing library resources.
Persistent Identifierhttp://hdl.handle.net/10722/164694

 

DC FieldValueLanguage
dc.contributor.authorRajagopal, Sen_US
dc.contributor.authorKwan, ACMen_US
dc.date.accessioned2012-09-20T08:08:03Z-
dc.date.available2012-09-20T08:08:03Z-
dc.date.issued2012en_US
dc.identifier.citationInformation, Technology and Educational Change, 2012en_US
dc.identifier.urihttp://hdl.handle.net/10722/164694-
dc.description.abstractThis paper describes the theoretical design of a Library Recommendation System, employing k- means clustering Data Mining algorithm, with subject headings of borrowed items as the basis for generating pertinent recommendations. Sample data from the University of Hong Kong Libraries (HKUL) has been used in a Quantitative approach to study the existing Library Information System, Innopac. Data Warehousing and Data Mining (k-means clustering) techniques are discussed. The primary benefit of the system is higher quality of academic research ensuing from better search results. Personalization improves individual effectiveness of learners and overall in better utilizing library resources.-
dc.languageengen_US
dc.publisherCentre for Information Technology in Education, Faculty of Education, University of Hong Kong. The Journal's web site is located at http://ejournal.cite.hku.hk/-
dc.relation.ispartofInformation, Technology and Educational Changeen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectRecommender Systems-
dc.subjectOPAC-
dc.subjectSubject searching-
dc.subjectData Mining-
dc.subjectLibrary Technology-
dc.titleBook Recommendation System using Data Mining for the University of Hong Kong Librariesen_US
dc.typeArticleen_US
dc.identifier.emailKwan, ACM: cmkwan@hku.hken_US
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros209443en_US
dc.publisher.placeHong Kong-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats