Using Linked Open Data in Recommender Systems

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Using Linked Open Data in Recommender Systems

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Using Linked Open Data in Recommender Systems

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics

ISBN

978-1-4503-3293-4

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

"17_1"-"17_6"

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Limassol, Cyprus

Datum konání akce

13. 7. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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