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dc.contributor.authorKhalifa, C.en
dc.contributor.authorChin, K. O.en
dc.contributor.authorAlfred, R.en
dc.contributor.authorAnthony, Patriciaen
dc.date.accessioned2016-07-05T03:39:34Z
dc.date.issued2014-04en
dc.identifier.citationKhalifa, Chin, K., Rayner, & Patricia. (2014). Document Recommender Agent Based on Hybrid Approach. International Journal of Machine Learning and Computing, 4(2), 151-156. doi:10.7763/IJMLC.2014.V4.404en
dc.identifier.urihttps://hdl.handle.net/10182/7056
dc.description.abstractAs Internet continues to grow, user tends to rely heavily on search engines. However, these search engines tend to generate a huge number of search results and potentially making it difficult for users to find the most relevant sites. This has resulted in search engines losing their usefulness. These users might be academicians who are searching for relevant academic papers within their interests. The need for a system that can assist in choosing the most relevant papers among the long list of results presented by search engines becomes crucial. In this paper, we propose Document Recommender Agent, that can recommend the most relevant papers based on the academician’s interest. This recommender agent adopts a hybrid recommendation approach. In this paper we also show that recommendation based on the proposed hybrid approach is better that the content-based and the collaborative approaches.en
dc.format.extent151-156en
dc.languageEnglishen
dc.language.isoenen
dc.publisherIACSIT Pressen
dc.relationThe original publication is available from - IACSIT Press - https://doi.org/10.7763/IJMLC.2014.V4.404 - http://www.ijmlc.org/papers/404-LC051.pdfen
dc.relation.urihttp://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=44&id=442en
dc.relation.urihttps://doi.org/10.7763/IJMLC.2014.V4.404en
dc.rightsCopyright © 2008-2015. International Journal of Machine Learning and Computing. All rights reserved.en
dc.subjectDocument recommender agenten
dc.subjectagent technologyen
dc.subjectinformation retrievalen
dc.titleDocument recommender agent based on hybrid approachen
dc.typeJournal Article
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Environment, Society and Designen
lu.contributor.unitDepartment of Environmental Managementen
lu.contributor.uniten
lu.contributor.uniten
dc.identifier.doi10.7763/IJMLC.2014.V4.404en
dc.subject.anzsrc08 Information and Computing Sciencesen
dc.subject.anzsrc080704 Information Retrieval and Web Searchen
dc.relation.isPartOfInternational Journal of Machine Learning and Computingen
pubs.issue2en
pubs.organisational-group/LU
pubs.organisational-group/LU/Faculty of Environment, Society and Design
pubs.organisational-group/LU/Faculty of Environment, Society and Design/DEM
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/2018 PBRF Staff group
pubs.publication-statusPublisheden
pubs.publisher-urlhttp://www.ijmlc.org/papers/404-LC051.pdfen
pubs.volume4en
dc.identifier.eissn2010-3700en
lu.identifier.orcid0000-0002-4991-3340


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