Personalised Recommendations and Profile Based Re-ranking Improve Distribution of Student Opportunities
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00374425" target="_blank" >RIV/68407700:21240/23:00374425 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1007/978-3-031-42519-6_21" target="_blank" >https://doi.org/10.1007/978-3-031-42519-6_21</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-42519-6_21" target="_blank" >10.1007/978-3-031-42519-6_21</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Personalised Recommendations and Profile Based Re-ranking Improve Distribution of Student Opportunities
Popis výsledku v původním jazyce
Modern technical universities help students get practical experience. They educate thousands of students and it is hard for them to connect individual students with relevant industry experts and opportunities. This article aims to solve this problem by designing a matchmaking procedure powered by a recommendation system, an ontology, and knowledge graphs. We suggest improving recommendations and reducing the cold-start problem with a re-ranking module based on student educational profiles for students who opt-in. Each student profile is represented as a knowledge graph derived from the successfully completed courses of the individual. The system was tested in an online experiment and demonstrated that recommendations based on student educational profiles and their interaction history significantly improve conversion rates over non-personalised offers.
Název v anglickém jazyce
Personalised Recommendations and Profile Based Re-ranking Improve Distribution of Student Opportunities
Popis výsledku anglicky
Modern technical universities help students get practical experience. They educate thousands of students and it is hard for them to connect individual students with relevant industry experts and opportunities. This article aims to solve this problem by designing a matchmaking procedure powered by a recommendation system, an ontology, and knowledge graphs. We suggest improving recommendations and reducing the cold-start problem with a re-ranking module based on student educational profiles for students who opt-in. Each student profile is represented as a knowledge graph derived from the successfully completed courses of the individual. The system was tested in an online experiment and demonstrated that recommendations based on student educational profiles and their interaction history significantly improve conversion rates over non-personalised offers.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
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Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023)
ISBN
978-3-031-42518-9
ISSN
2367-3370
e-ISSN
2367-3389
Počet stran výsledku
11
Strana od-do
217-227
Název nakladatele
Springer
Místo vydání
Cham
Místo konání akce
Salamanca
Datum konání akce
5. 9. 2023
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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