Enhancing Recommender System with Linked Open Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10139475" target="_blank" >RIV/00216208:11320/13:10139475 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-40769-7_42" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-40769-7_42</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-40769-7_42" target="_blank" >10.1007/978-3-642-40769-7_42</a>

Result language
angličtina
Original language name
Enhancing Recommender System with Linked Open Data
Original language description
In this paper, we present an innovative method to use Linked Open Data (LOD) to improve content based recommender systems. We have selected the domain of secondhand bookshops, where recommending is extraordinary difficult because of high ratio of objects/users, lack of significant attributes and small number of the same items in stock. Those difficulties prevents us from successfully apply both collaborative and common content based recommenders. We have queried Czech language mutation of DBPedia in order to receive additional attributes of objects (books) to reveal nontrivial connections between them. Our approach is general and can be applied on other domains as well. Experiments show that enhancing recommender system with LOD can significantly improve its results in terms of object similarity computation and top-k objects recommendation. The main drawback hindering widespread of such systems is probably missing data about considerable portion of objects, which can however vary acros
Czech name
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Czech description
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Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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