Content-based recommender system for textual documents written in Croatian

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Titel: Content-based recommender system for textual documents written in Croatian
Autoren: Semanjski, Ivana, Kavran, Zvonko, Jolic, Natalija, Andelovic, Neven, Cvitic, Ivan, Gović, Marko
Weitere Verfasser: Laux, Friedrich
Quelle: The Second International Conference on Data Analytics, Proceedings ; ISSN: 2308-4464 ; ISBN: 9781612082950
Verlagsinformationen: IARIA
Publikationsjahr: 2013
Bestand: Ghent University Academic Bibliography
Schlagwörter: Technology and Engineering, k-nearest neighbour, recommender system, text mining, document-term matrix, content-based classification
Beschreibung: The paper describes a content-based recommender system that classifies textual documents written in Croatian. We describe how documents are pre- processed, including procedures of dimensionality reduction, selection of stop-words and creation of document-term matrix. For the text classification, a combination of v-fold cross validation and k - nearest neighbours (kNN) methods is used. This way, the ‘optimal’ value of k is firstly analyzed, and the results of v-fold cross validation are applied for the selection of value k. Results are given in the form of classification error analysis.
Publikationsart: conference object
Dateibeschreibung: application/pdf
Sprache: English
ISBN: 978-1-61208-295-0
1-61208-295-5
Relation: https://biblio.ugent.be/publication/4233486; https://biblio.ugent.be/publication/4233486/file/01HSGH06BYC4TP3SHA25GDJ43D
Verfügbarkeit: https://biblio.ugent.be/publication/4233486
https://hdl.handle.net/1854/LU-4233486
https://biblio.ugent.be/publication/4233486/file/01HSGH06BYC4TP3SHA25GDJ43D
Rights: info:eu-repo/semantics/restrictedAccess
Dokumentencode: edsbas.9A4787E7
Datenbank: BASE
Beschreibung
Abstract:The paper describes a content-based recommender system that classifies textual documents written in Croatian. We describe how documents are pre- processed, including procedures of dimensionality reduction, selection of stop-words and creation of document-term matrix. For the text classification, a combination of v-fold cross validation and k - nearest neighbours (kNN) methods is used. This way, the ‘optimal’ value of k is firstly analyzed, and the results of v-fold cross validation are applied for the selection of value k. Results are given in the form of classification error analysis.
ISBN:9781612082950
1612082955