Content-based recommender system for online stores using expert system
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F18%3AA1901VEM" target="_blank" >RIV/61988987:17310/18:A1901VEM - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/AIKE.2018.00036" target="_blank" >http://dx.doi.org/10.1109/AIKE.2018.00036</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/AIKE.2018.00036" target="_blank" >10.1109/AIKE.2018.00036</a>
Alternative languages
Result language
angličtina
Original language name
Content-based recommender system for online stores using expert system
Original language description
This paper deals with a content-based recommender system for online stores using the expert system. We propose an algorithm which adapts the content based on user preferences and the content viewed by the user. The main goal of the recommender system is to propose and deliver suitable content to the user. One of the goals of the proposed recommender system is to decrease the cold start effect. At the end of the paper, the proposed system is experimentally verified.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
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 AIKE 2018
ISBN
978-153869555-5
ISSN
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e-ISSN
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Number of pages
2
Pages from-to
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Publisher name
IEEE
Place of publication
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Event location
Laguna Hills
Event date
Sep 26, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000454624300027