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Developing a Personalized Study Program Recommender

dc.contributor.authorScheffler, Marc
dc.contributor.authorDieing, Thilo I.
dc.contributor.authorCohausz, Lea
dc.contributor.editorKiesler, Natalie
dc.contributor.editorSchulz, Sandra
dc.date.accessioned2024-10-21T10:40:35Z
dc.date.available2024-10-21T10:40:35Z
dc.date.issued2024
dc.description.abstractThis paper presents a recommender system designed to match prospective students with study programs in Baden-Württemberg, Germany, streamlining the selection process by providing personalized recommendations based on user queries. Utilizing data from approximately 1,500 study programs and employing natural language processing and machine learning techniques, specifically the German fastText model for word embeddings, our system captures the semantic relationships between user queries and program descriptions. We evaluated the system’s performance using both manual test cases and automated validation methods. The manual evaluation involved subjective assessments by multiple raters, while the automated approach utilized self-supervised keyword-based approaches. The results demonstrate the system’s effectiveness in enhancing the study program selection process.en
dc.identifier.doi10.18420/delfi2024-ws-33
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45049
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofProceedings of DELFI Workshops 2024
dc.relation.ispartofseriesDELFI
dc.subjecteducation
dc.subjectrecommender system
dc.subjectstudy program recommendation
dc.subjectNLP
dc.subjectfastText
dc.subjectembed- dings
dc.subjectBERUFENET
dc.titleDeveloping a Personalized Study Program Recommenderen
dc.typeText/Conference Paper
mci.conference.date09.-11. September 2024
mci.conference.locationFulda
mci.conference.sessiontitleDELFI: Workshop
mci.document.qualitydigidoc
mci.reference.pages241-248

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