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A personalized search engine based on correlation clustering method

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dc.citedby1
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorAlattar B.en_US
dc.contributor.authorNorwawi N.M.en_US
dc.date.accessioned2024-05-28T08:44:45Z
dc.date.available2024-05-28T08:44:45Z
dc.date.issued2016
dc.description.abstractNowadays, search engines tend to use latest technologies in enhancing the personalization of web searches, which leads to better understanding of user requirements. This paper aims to address the problem of enhancing the efficiency of search system by mining data logs. Search logs are associated with all the interactions that have been done between the user and the search engine including the query, the resulted pages and the selection. The paper also aims minimize the ambiguity within the query whilst the partitioning-based clustering aims to reduce the search space by dividing the data into groups that contain similar objects. To do so, this paper conduct several experiments to evaluate correlation clustering method. The method of this paper includes a pre-processing phase, which in turn involves tokenization, stop-words removal, and stemming. In addition, we evaluates the impact of the two similarity/distance measures (Cosine similarity and Jaccard coefficient) on the results of the correlation clustering method. Experimental results obtained are quite satisfactory in terms of the Precision, Recall and F-score. � 2005-2016 JATIT & LLS. All rights reserved.
dc.description.natureFinalen_US
dc.identifier.epage352
dc.identifier.issn19928645
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85001907751
dc.identifier.spage345
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85001907751&partnerID=40&md5=0bd85ad131f9ea063b17969fbb193533
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9388
dc.identifier.volume93
dc.languageEnglish
dc.language.isoen_US
dc.publisherAsian Research Publishing Networken_US
dc.relation.ispartofJournal of Theoretical and Applied Information Technology
dc.sourceScopus
dc.subjectCorrelation clustering algorithmen_US
dc.subjectPersonalized search engineen_US
dc.subjectSimilarity/distance measuresen_US
dc.titleA personalized search engine based on correlation clustering method
dc.typeArticleen_US
dspace.entity.typePublication

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