This paper addresses the need to better support dyslexic students in higher education using data-driven methods. Our approach lies in modeling relationships between dyslexic students, problems inherent to their condition, and potential solutions. The proposed graph database integrates multiple data sources, in different formats and languages, and captures complex relationships between entities that are not identifiable when considering each data source independently. This paper's main contribution is a hybrid recommender system that first filters potential solutions through the navigation of the modeled graph, then utilizes a Neural Network to solve an ordinal classification problem: effectively ranking the filtered recommendations based on their predicted usefulness. Several documented approaches to solving the ranking algorithm's prediction task were implemented and compared. The models that achieved the highest ranking accuracy, approximately 74%, were 3-Layer Neural Networks trained with Ordinal Log-Loss and self-guided EMD² loss. In summary, our work not only facilitates the identification of patterns essential for crafting personalized recommendations to address the most severe difficulties of dyslexic students, but also establishes a structured foundation for the scalable integration of additional data sources. These results strongly support future research and application development related to dyslexia in higher education.