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Content-based Recommender System for Textual Documents Written in Croatian

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Podrobná bibliografie
Název:	Content-based Recommender System for Textual Documents Written in Croatian
Autoři:	Kavran, Natalija, Kavran, Zvonko, Cvitić, Ivan, Šemanjski, Ivana
Informace o vydavateli:	2013.
Rok vydání:	2013
Témata:	recommender system, k-nearest neighbour, text mining, content-based classification, document-term matrix
Popis:	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.
Druh dokumentu:	Conference object
Přístupové číslo:	edsair.dris...01492..28a2c381f360dd797051257b7a1d2532
Databáze:	OpenAIRE

Popis
Abstrakt:	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.