Reducing Cold Start Problems in Educational Recommender Systems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F16%3A00302590" target="_blank" >RIV/68407700:21240/16:00302590 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7727600&isnumber=7726591" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7727600&isnumber=7726591</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2016.7727600" target="_blank" >10.1109/IJCNN.2016.7727600</a>

Result language
angličtina
Original language name
Reducing Cold Start Problems in Educational Recommender Systems
Original language description
Educational data can help us to personalise university information systems. In this paper, we show how educational data can be used to improve the performance of interaction-based recommender systems. Educational data is transformed to student profiles helping to prevent cold start problems when recommending projects to students with few user interactions. Our results show that our hybrid interaction based recommender boosted by educational profiles significantly outperforms bestseller recommendation, which is a mainstream recommendation method for cold start users.
Czech name
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Czech description
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2016 International Joint Conference on Neural Networks (IJCNN)
ISBN
978-1-5090-0620-5
ISSN
2161-4407
e-ISSN
—
Number of pages
7
Pages from-to
3143-3149
Publisher name
American Institute of Physics and Magnetic Society of the IEEE
Place of publication
San Francisco
Event location
Vancouver
Event date
Jul 24, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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