Down Syndrome and Autism Resource Recommendation Based on Graphs

This article describes the development of a graph-based recommendation system for children with Down syndrome and Autism in Cuenca, Ecuador, addressing the high prevalence of these disorders in public institutions. Based on recommender system principles and digital resource repositories, the goal is to connect users with tailored educational and therapeutic materials, promoting quality of life and inclusion. The system integrates child profiles, resource information, and disorder characteristics into a knowledge graph, applying search and classification algorithms to generate personalized recommendations. Using NoSQL databases like MongoDB and Neo4j ensures efficient, reliable data storage and processing, with Neo4j’s graph-oriented algorithms enhancing search performance. Evaluation through surveys revealed that 80% of respondents approved of the recommendations, validating the system’s relevance and effectiveness. By facilitating access to resources adapted to the specific needs of children with intellectual and developmental disabilities, the system fosters engagement in activities and better understanding of available materials. Feedback gathered will guide future enhancements, supporting continued refinement through real-world data and collaboration with professionals. This approach demonstrates potential for creating a more inclusive and enriching environment, improving resource selection and accessibility for children with Down syndrome and Autism.