Personalised Recommendations and Profile Based Re-ranking Improve Distribution of Student Opportunities
Autoři
Rok
2023
Publikováno
Lecture Notes in Networks and Systems. 2023, 2023(748), 217-227. ISSN 2367-3370.
Typ
Článek
Anotace
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.