Developing a Personalized Study Program Recommender

Abstract

This 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.