Recommender systems in knowledge-mining

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F17%3AA1801QY1" target="_blank" >RIV/61988987:17310/17:A1801QY1 - isvavai.cz</a>

Výsledek na webu

<a href="http://aip.scitation.org/doi/abs/10.1063/1.4992225" target="_blank" >http://aip.scitation.org/doi/abs/10.1063/1.4992225</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/1.4992225" target="_blank" >10.1063/1.4992225</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Recommender systems in knowledge-mining

Popis výsledku v původním jazyce

The subject of the paper is to analyse the possibilities of application of recommender systems in the field of data mining. The work focuses on three basic types of recommender systems (collaborative, content-based and hybrid). The goal of the article is to evaluate which of these three concepts of recommender systems provides forecast with the lowest error rate in the domain of recommending movies. This target is fulfilled by the practical part of the work - at first, the own recommender system was designed and created, capable of obtaining movies recommendation from the database based on the user's preferences. Next, we verified experimentally which recommender system produces more accurate results.

Název v anglickém jazyce

Recommender systems in knowledge-mining

Popis výsledku anglicky

The subject of the paper is to analyse the possibilities of application of recommender systems in the field of data mining. The work focuses on three basic types of recommender systems (collaborative, content-based and hybrid). The goal of the article is to evaluate which of these three concepts of recommender systems provides forecast with the lowest error rate in the domain of recommending movies. This target is fulfilled by the practical part of the work - at first, the own recommender system was designed and created, capable of obtaining movies recommendation from the database based on the user's preferences. Next, we verified experimentally which recommender system produces more accurate results.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

AIP Conference Proceedings

ISBN

978-073541538-6

ISSN

0094-243X

e-ISSN

—

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

American Institute of Physics Inc.

Místo vydání

—

Místo konání akce

Rhodes; Greece

Datum konání akce

26. 9. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000410159800075