Recommender systems in knowledge-mining

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Recommender systems in knowledge-mining
Original language description
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.
Czech name
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Czech description
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Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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