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Pregled bibliografske jedinice broj: 681376

Content-based Recommender System for Textual Documents Written in Croatian


Ćavar, Ivana; Kavran, Zvonko; Jolić, Natalija; Anđelović, Neven; Cvitić, Ivan; Gović, Marko
Content-based Recommender System for Textual Documents Written in Croatian // DATA ANALYTICS 2013, The Second International Conference on Data Analytics / Friedrich Laux, Reutlingen University, Germany (ur.).
Porto, Portugal: IARIA, 2013. str. 25-29 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 681376 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Content-based Recommender System for Textual Documents Written in Croatian

Autori
Ćavar, Ivana ; Kavran, Zvonko ; Jolić, Natalija ; Anđelović, Neven ; Cvitić, Ivan ; Gović, Marko

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
DATA ANALYTICS 2013, The Second International Conference on Data Analytics / Friedrich Laux, Reutlingen University, Germany - Porto, Portugal : IARIA, 2013, 25-29

ISBN
978-1-61208-295-0

Skup
DATA ANALYTICS 2013, The Second International Conference on Data Analytics

Mjesto i datum
Porto, Portugal, September 29, 2013 - October 3, 2013

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
text mining; recommender system; k-nearest neighbour; content-based classification; document-term matrix.

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Tehnologija prometa i transport



POVEZANOST RADA


Projekti:
135-1352586-2588 - Integracija sustava intermodalnog vodnog prometa u europskoj transportnoj mreži (Jolić, Natalija, MZOS ) ( POIROT)
135-1352586-2591 - Definiranje intermodalnih transportnih koridora višekriterijskim odlučivanjem (Kavran, Zvonko, MZOS ) ( POIROT)

Ustanove:
Fakultet prometnih znanosti, Zagreb

Profili:

Avatar Url Zvonko Kavran (autor)

Avatar Url Ivana Šemanjski (autor)

Avatar Url Natalija Kavran (autor)

Avatar Url Ivan Cvitić (autor)

Citiraj ovu publikaciju

Ćavar, Ivana; Kavran, Zvonko; Jolić, Natalija; Anđelović, Neven; Cvitić, Ivan; Gović, Marko
Content-based Recommender System for Textual Documents Written in Croatian // DATA ANALYTICS 2013, The Second International Conference on Data Analytics / Friedrich Laux, Reutlingen University, Germany (ur.).
Porto, Portugal: IARIA, 2013. str. 25-29 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Ćavar, I., Kavran, Z., Jolić, N., Anđelović, N., Cvitić, I. & Gović, M. (2013) Content-based Recommender System for Textual Documents Written in Croatian. U: Friedrich Laux, Reutlingen University, Germany (ur.)DATA ANALYTICS 2013, The Second International Conference on Data Analytics.
@article{article, year = {2013}, pages = {25-29}, keywords = {text mining, recommender system, k-nearest neighbour, content-based classification, document-term matrix.}, isbn = {978-1-61208-295-0}, title = {Content-based Recommender System for Textual Documents Written in Croatian}, keyword = {text mining, recommender system, k-nearest neighbour, content-based classification, document-term matrix.}, publisher = {IARIA}, publisherplace = {Porto, Portugal} }
@article{article, year = {2013}, pages = {25-29}, keywords = {text mining, recommender system, k-nearest neighbour, content-based classification, document-term matrix.}, isbn = {978-1-61208-295-0}, title = {Content-based Recommender System for Textual Documents Written in Croatian}, keyword = {text mining, recommender system, k-nearest neighbour, content-based classification, document-term matrix.}, publisher = {IARIA}, publisherplace = {Porto, Portugal} }




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