Using Linked Open Data in Recommender Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10314886" target="_blank" >RIV/00216208:11320/15:10314886 - isvavai.cz</a>
Result on the web
<a href="http://dl.acm.org/citation.cfm?doid=2797115.2797128" target="_blank" >http://dl.acm.org/citation.cfm?doid=2797115.2797128</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/2797115.2797128" target="_blank" >10.1145/2797115.2797128</a>
Alternative languages
Result language
angličtina
Original language name
Using Linked Open Data in Recommender Systems
Original language description
In this paper, we present our work in progress on using LOD data to enhance recommending on existing e-commerce sites. We imagine a situation of e-commerce website employing content-based or hybrid recommendation. Such recommending algorithms need relevant object attributes to produce useful recommendations. However, on some domains, usable attributes may be difficult to fill in manually and yet accessible from LOD cloud. A pilot study was conducted on the domain of secondhand bookshops. In this domain,recommending is extraordinary difficult because of high ratio between objects and users, lack of significant attributes and limited availability of items. Both collaborative filtering and content-based recommendation applicability is questionable underthis conditions. We queried both Czech and English language edition of DBPedia in order to receive additional information about objects (books) and used various recommending algorithms to learn user preferences. Our approach is general an
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics
ISBN
978-1-4503-3293-4
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
"17_1"-"17_6"
Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Limassol, Cyprus
Event date
Jul 13, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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